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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2106.11188"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1904.02101"/>
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	<rdf:li rdf:resource="https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1629942"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1703.04467"/>
	<rdf:li rdf:resource="https://kieranhealy.org/blog/archives/2019/03/22/a-quick-and-tidy-look-at-the-2018-gss/"/>
	<rdf:li rdf:resource="https://www.cambridge.org/9781108705295"/>
	<rdf:li rdf:resource="https://onlinelibrary.wiley.com/doi/book/10.1002/9781119991595"/>
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	<rdf:li rdf:resource="http://statsthinking21.org/"/>
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	<rdf:li rdf:resource="http://socviz.co/"/>
	<rdf:li rdf:resource="http://docs.renjin.org/en/latest/package/index.html"/>
	<rdf:li rdf:resource="http://journal.sjdm.org/17/17217/jdm17217.html"/>
	<rdf:li rdf:resource="http://shiny.stat.cmu.edu:3838/hseltman/IROL/"/>
	<rdf:li rdf:resource="http://www.stat.cmu.edu/~hseltman/shinyTex/"/>
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	<rdf:li rdf:resource="http://stat545-ubc.github.io/bit007_draw-the-rest-of-the-owl.html"/>
	<rdf:li rdf:resource="https://twitter.com/JennyBryan/status/704779515558400000"/>
	<rdf:li rdf:resource="http://www.mosaic-web.org/go/StatisticalModeling/"/>
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  </channel><item rdf:about="https://tensorflow.rstudio.com/">
    <title>TensorFlow for R</title>
    <dc:date>2025-08-22T13:41:43+00:00</dc:date>
    <link>https://tensorflow.rstudio.com/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R computational_statistics neural_networks to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da25d2c2f4b5/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_and_generative_ai"/>
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</item>
<item rdf:about="https://www.manning.com/books/deep-learning-with-r-third-edition">
    <title>Deep Learning with R, Third Edition - François Chollet, Tomasz Kalinowski</title>
    <dc:date>2025-08-22T13:40:30+00:00</dc:date>
    <link>https://www.manning.com/books/deep-learning-with-r-third-edition</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Deep Learning with R, Third Edition introduces deep learning from scratch with examples that use the R language and the Keras library. Each chapter offers practical code examples that build your understanding of deep learning layer by layer. You’ll appreciate the intuitive explanations, crisp illustrations, and clear examples. In this expanded third edition you’ll find fresh chapters on the transformers architecture, building your own GPT-like large language model, and image generation with diffusion models. Plus, even DL veterans will benefit from the insightful explanations on the nature of deep learning."

--- November publication => not suitable for this iteration of the class, but for the next one (if that should happen).  I should probably buy this though.]]></description>
<dc:subject>R computational_statistics neural_networks to_teach:statistics_and_generative_ai books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ec988fafd3f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_and_generative_ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
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</item>
<item rdf:about="https://CRAN.R-project.org/package=neuralnet">
    <title>CRAN: Package neuralnet</title>
    <dc:date>2025-08-22T13:38:12+00:00</dc:date>
    <link>https://CRAN.R-project.org/package=neuralnet</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented."]]></description>
<dc:subject>R computational_statistics neural_networks to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7a78fcb5f65b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
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<item rdf:about="https://CRAN.R-project.org/package=deepnet">
    <title>CRAN: Package deepnet</title>
    <dc:date>2025-08-22T13:37:28+00:00</dc:date>
    <link>https://CRAN.R-project.org/package=deepnet</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on."]]></description>
<dc:subject>R computational_statistics neural_networks to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ac773d1d8513/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
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<item rdf:about="https://torch.mlverse.org/">
    <title>torch for R</title>
    <dc:date>2025-08-22T13:36:21+00:00</dc:date>
    <link>https://torch.mlverse.org/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R computational_statistics neural_networks to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8d5d24d64dbd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
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</item>
<item rdf:about="https://arelbundock.com/r_idioms.html">
    <title>data.table vs. base vs. dplyr</title>
    <dc:date>2025-03-16T19:23:32+00:00</dc:date>
    <link>https://arelbundock.com/r_idioms.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This page presents a side-by-side comparison of common data manipulation operations in R in three idioms: data.table, base, and dplyr. This allows you to compare syntax and understand how to accomplish tasks across these popular frameworks."]]></description>
<dc:subject>R to_teach:statcomp have_skimmed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92613b72bc57/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://klmr.me/box/">
    <title>‘box’: Write Reusable, Composable and Modular R Code • box</title>
    <dc:date>2024-11-05T14:04:26+00:00</dc:date>
    <link>https://klmr.me/box/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["‘box’ allows organising R code in a more modular way, via two mechanisms:
"-It enables writing modular code by treating files and folders of R code as independent (potentially nested) modules, without requiring the user to wrap reusable code into packages.
"-It provides a new syntax to import reusable code (both from packages and modules) that is more powerful and less error-prone than library by allowing explicit control over what names to import, and by restricting the scope of the import."]]></description>
<dc:subject>R programming via:kjhealy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:17ac8c60d9a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
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<item rdf:about="https://joss.theoj.org/papers/10.21105/joss.04522">
    <title>Journal of Open Source Software: haldensify: Highly adaptive lasso conditional density estimation in R</title>
    <dc:date>2022-12-09T20:04:28+00:00</dc:date>
    <link>https://joss.theoj.org/papers/10.21105/joss.04522</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The haldensify R package serves as a toolbox for nonparametric conditional density estimation
based on the highly adaptive lasso, a flexible nonparametric algorithm for the estimation of
functional statistical parameters (e.g., conditional mean, hazard, density). Building upon an
earlier proposal (Dı́az & van der Laan, 2011), haldensify leverages the relationship between
the hazard and density functions to estimate the latter by applying pooled hazard regression to
a synthetic repeated measures dataset created from the input data, relying upon the framework
of cross-validated loss-based estimation to yield an optimal estimator (Dudoit & van der Laan,
2005; van der Laan et al., 2004). While conditional density estimation is a fundamental problem
in statistics, arising naturally in a variety of applications (including machine learning), it plays
a critical role in estimating the causal effects of continuous- or ordinal-valued treatments. In
such settings this covariate-conditional treatment density has been termed the generalized
propensity score (Hirano & Imbens, 2004; Imai & Van Dyk, 2004), and, like its analog for
binary treatments (Rosenbaum & Rubin, 1983), serves as a key ingredient in developing both
inverse probability weighted and doubly robust estimators of causal effects (Dı́az & van der
Laan, 2012, 2018; Haneuse & Rotnitzky, 2013; Hejazi et al., 2022)"]]></description>
<dc:subject>density_estimation R lasso sparsity van_der_laan.mark in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d6b76808e7da/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:density_estimation"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lasso"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sparsity"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journal.r-project.org/archive/2014/RJ-2014-023/index.html">
    <title>phaseR: An R Package for Phase Plane Analysis of Autonomous ODE Systems (Grayling, 2014)</title>
    <dc:date>2022-12-02T15:46:52+00:00</dc:date>
    <link>https://journal.r-project.org/archive/2014/RJ-2014-023/index.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When modelling physical systems, analysts will frequently be confronted by differential equations which cannot be solved analytically. In this instance, numerical integration will usually be the only way forward. However, for autonomous systems of ordinary differential equations (ODEs) in one or two dimensions, it is possible to employ an instructive qualitative analysis foregoing this requirement, using so-called phase plane methods. Moreover, this qualitative analysis can even prove to be highly useful for systems that can be solved analytically, or will be solved numerically anyway. The package phaseR allows the user to perform such phase plane analyses: determining the stability of any equilibrium points easily, and producing informative plots."]]></description>
<dc:subject>to:NB have_skimmed R dynamical_systems to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a925ecd44c4a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v101i08">
    <title>Inference Tools for Markov Random Fields on Lattices: The R Package mrf2d | Journal of Statistical Software</title>
    <dc:date>2022-06-15T07:49:49+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v101i08</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Markov random fields on two-dimensional lattices are behind many image analysis methodologies. mrf2d provides tools for statistical inference on a class of discrete stationary Markov random field models with pairwise interaction, which includes many of the popular models such as the Potts model and texture image models. The package introduces representations of dependence structures and parameters, visualization functions and efficient (C++-based) implementations of sampling algorithms, common estimation methods and other key features of the model, providing a useful framework to implement algorithms and working with the model in general. This paper presents a description and details of the package, as well as some reproducible examples of usage."]]></description>
<dc:subject>to:NB random_fields monte_carlo R statistical_inference_for_stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1a0c75188ebb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:monte_carlo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cran.r-project.org/web/packages/villager/index.html">
    <title>CRAN - Package villager</title>
    <dc:date>2022-06-13T16:55:28+00:00</dc:date>
    <link>https://cran.r-project.org/web/packages/villager/index.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs."

--- No NetLogo, supposedly.
--- Last tag for (a) Schelling model, and (b) Knight / Axtell & Young / O'Connor style models of the evolution of unequal institutions.  (That last is probably insanely ambitious.)]]></description>
<dc:subject>via:? to_read where_to_read_means_to_mess_around_with agent-based_models R to_teach:complexity-and-inference to_teach:data_over_space_and_time to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:040146b84cb8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:where_to_read_means_to_mess_around_with"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.brodrigues.co/blog/2022-04-11-monads/">
    <title>Why you should(n't) care about Monads if you're an R programmer</title>
    <dc:date>2022-04-13T12:29:19+00:00</dc:date>
    <link>https://www.brodrigues.co/blog/2022-04-11-monads/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Wait wait wait, _that's_ a monad?  I've been using them since CS60A in the spring of 1991?  I've _taught_ The Kids to use them in R?  I don't know whether to denounce myself as an imposter or feel absurdly smug.]]></description>
<dc:subject>programming R to_teach:statcomp have_read via:kjhealy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c2d42744f2e2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kjhealy.github.io/gssr/index.html">
    <title>US General Social Survey (GSS) Data for R • gssr</title>
    <dc:date>2021-09-29T13:19:34+00:00</dc:date>
    <link>https://kjhealy.github.io/gssr/index.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R data_sets surveys to_teach:undergrad-ADA to_teach:data_over_space_and_time to_teach:statistics_of_inequality_and_discrimination healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:91dd691c9d70/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:surveys"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v058i02">
    <title>R Marries NetLogo: Introduction to the RNetLogo Package | Thiele | Journal of Statistical Software</title>
    <dc:date>2021-08-29T04:59:45+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v058i02</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The RNetLogo package delivers an interface to embed the agent-based modeling platform NetLogo into the R environment with headless (no graphical user interface) or interactive GUI mode. It provides functions to load models, execute commands, push values, and to get values from NetLogo reporters. Such a seamless integration of a widely used agent-based modeling platform with a well-known statistical computing and graphics environment opens up various possibilities. For example, it enables the modeler to design simulation experiments, store simulation results, and analyze simulation output in a more systematic way. It can therefore help close the gaps in agent-based modeling regarding standards of description and analysis. After a short overview of the agent-based modeling approach and the software used here, the paper delivers a step-by-step introduction to the usage of the RNetLogo package by examples."


--- Last tag is if I want them to try coding up Schelling or similar.]]></description>
<dc:subject>R agent-based_models to_teach:complexity-and-inference to_teach:data_over_space_and_time to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:95b12579d47d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v098i10">
    <title>Continuous Time Individual-Level Models of Infectious Disease: Package EpiILMCT | Almutiry | Journal of Statistical Software</title>
    <dc:date>2021-06-30T18:41:22+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v098i10</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SIN R) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus disease."
]]></description>
<dc:subject>to:NB epidemic_models R</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c71baefab35a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.11188">
    <title>[2106.11188] maars: Tidy Inference under the 'Models as Approximations' Framework in R</title>
    <dc:date>2021-06-25T15:07:29+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.11188</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Linear regression using ordinary least squares (OLS) is a critical part of every statistician's toolkit. In R, this is elegantly implemented via lm() and its related functions. However, the statistical inference output from this suite of functions is based on the assumption that the model is well specified. This assumption is often unrealistic and at best satisfied approximately. In the statistics and econometrics literature, this has long been recognized and a large body of work provides inference for OLS under more practical assumptions. This can be seen as model-free inference. In this paper, we introduce our package maars ("models as approximations") that aims at bringing research on model-free inference to R via a comprehensive workflow. The maars package differs from other packages that also implement variance estimation, such as sandwich, in three key ways. First, all functions in maars follow a consistent grammar and return output in tidy format, with minimal deviation from the typical lm() workflow. Second, maars contains several tools for inference including empirical, multiplier, residual bootstrap, and subsampling, for easy comparison. Third, maars is developed with pedagogy in mind. For this, most of its functions explicitly return the assumptions under which the output is valid. This key innovation makes maars useful in teaching inference under misspecification and also a powerful tool for applied researchers. We hope our default feature of explicitly presenting assumptions will become a de facto standard for most statistical modeling in R."]]></description>
<dc:subject>to:NB linear_regression R to_teach:linear_models kith_and_kin kuchibhotla.arun re:TALR statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0908810c0902/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kuchibhotla.arun"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:TALR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.04997">
    <title>[2106.04997] ergm 4.0: New features and improvements</title>
    <dc:date>2021-06-10T02:12:34+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.04997</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the functionality and performance improvements in the 2021 ergm 4.0 release. These include more flexible handling of nodal covariates, operator terms that extend and simplify model specification, new models for networks with valued edges, improved handling of constraints on the sample space of networks, performance enhancements to the Markov chain Monte Carlo and maximum likelihood estimation algorithms, broader and faster searching for networks with certain target statistics using simulated annealing, and estimation with missing edge data. We also identify the new packages in the statnet suite that extend ergm's functionality to other network data types and structural features, and the robust set of online resources that support the statnet development process and applications."]]></description>
<dc:subject>to:NB exponential_family_random_graphs R morris.martina hunter.david_r. krivitsky.pavel_n. statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a3e675d9b65e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:exponential_family_random_graphs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:morris.martina"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hunter.david_r."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:krivitsky.pavel_n."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v098i08">
    <title>Analysis of Multiplex Social Networks with R | Magnani | Journal of Statistical Software</title>
    <dc:date>2021-06-07T02:49:47+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v098i08</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Multiplex social networks are characterized by a common set of actors connected through multiple types of relations. The multinet package provides a set of R functions to analyze multiplex social networks within the more general framework of multilayer networks, where each type of relation is represented as a layer in the network. The package contains functions to import/export, create and manipulate multilayer networks, implementations of several state-of-the-art multiplex network analysis algorithms, e.g., for centrality measures, layer comparison, community detection and visualization. Internally, the package is mainly written in native C++ and integrated with R using the Rcpp package."]]></description>
<dc:subject>to:NB network_data_analysis R to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b4e5a38a9140/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v098i04">
    <title>FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets | Zammit-Mangion | Journal of Statistical Software</title>
    <dc:date>2021-06-01T17:49:15+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v098i04</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["FRK is an R software package for spatial/spatio-temporal modeling and prediction with large datasets. It facilitates optimal spatial prediction (kriging) on the most commonly used manifolds (in Euclidean space and on the surface of the sphere), for both spatial and spatio-temporal fields. It differs from many of the packages for spatial modeling and prediction by avoiding stationary and isotropic covariance and variogram models, instead constructing a spatial random effects (SRE) model on a fine-resolution discretized spatial domain. The discrete element is known as a basic areal unit (BAU), whose introduction in the software leads to several practical advantages. The software can be used to (i) integrate multiple observations with different supports with relative ease; (ii) obtain exact predictions at millions of prediction locations (without conditional simulation); and (iii) distinguish between measurement error and fine-scale variation at the resolution of the BAU, thereby allowing for reliable uncertainty quantification. The temporal component is included by adding another dimension. A key component of the SRE model is the specification of spatial or spatio-temporal basis functions; in the package, they can be generated automatically or by the user. The package also offers automatic BAU construction, an expectation-maximization (EM) algorithm for parameter estimation, and functionality for prediction over any user-specified polygons or BAUs. Use of the package is illustrated on several spatial and spatio-temporal datasets, and its predictions and the model it implements are extensively compared to others commonly used for spatial prediction and modeling."]]></description>
<dc:subject>to:NB spatial_statistics spatio-temporal_statistics smoothing to_teach:data_over_space_and_time R computational_statistics statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:919801487141/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:smoothing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journal.r-project.org/archive/2013/RJ-2013-002/index.html">
    <title>Generalized Simulated Annealing for Global Optimization: The GenSA PackageThe R Journal</title>
    <dc:date>2021-04-18T13:58:40+00:00</dc:date>
    <link>https://journal.r-project.org/archive/2013/RJ-2013-002/index.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many problems in statistics, finance, biology, pharmacology, physics, mathematics, eco nomics, and chemistry involve determination of the global minimum of multidimensional functions. R packages for different stochastic methods such as genetic algorithms and differential evolution have been developed and successfully used in the R community. Based on Tsallis statistics, the R package GenSA was developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. In this paper we provide a brief introduction to the R package and demonstrate its utility by solving a non-convex portfolio optimization problem in finance and the Thomson problem in physics. GenSA is useful and can serve as a complementary tool to, rather than a replacement for, other widely used R packages for optimization."

--- If this work for me, will I have to think better about Tsallis statistics?
--- ETA: My disdain for Tsallis-ism is safe; this is simulated annealing with a Cauchy step-size distribution and polynomial acceptance probabilities.  It even seems to be working OK on my problem.  (Fingers crossed.)  The idea of controlling the clock time taken by the optimizer is nice but my computer seems to be ignoring that...]]></description>
<dc:subject>to:NB simulated_annealing optimization R re:codename:catherine_wheel</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:31bd014e45d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulated_annealing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:codename:catherine_wheel"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.05292">
    <title>[2104.05292] Computer Algebra in R with caracas</title>
    <dc:date>2021-04-15T02:32:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.05292</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The capability of R to do symbolic mathematics is enhanced by the caracas package. This package uses the Python computer algebra library SymPy as a back-end but caracas is tightly integrated in the R environment, thereby enabling the R user with symbolic mathematics within R. Key components of the caracas package are illustrated in this paper. Examples are taken from statistics and mathematics. The caracas package integrates well with e.g. Rmarkdown, and as such creation of scientific reports and teaching is supported."]]></description>
<dc:subject>R to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:858656511a44/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://notstatschat.rbind.io/2021/04/01/a-modest-proposal-for-matrix-multiplication/">
    <title>A modest proposal for matrix multiplication - Biased and Inefficient</title>
    <dc:date>2021-04-05T21:08:45+00:00</dc:date>
    <link>https://notstatschat.rbind.io/2021/04/01/a-modest-proposal-for-matrix-multiplication/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R lumley.thomas note_the_date</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a1ff73d9dc3d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lumley.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:note_the_date"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/CarrKnight/freelunch">
    <title>CarrKnight/freelunch: A R package containing some helper functions to estimate parameters for simulation models, test such estimations and plot them</title>
    <dc:date>2021-04-05T18:04:17+00:00</dc:date>
    <link>https://github.com/CarrKnight/freelunch</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R agent-based_models to_read to_try_out simulation simulation-based_inference to_teach:complexity-and-inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1f2b0d915a92/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_try_out"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation-based_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://awesomeopensource.com/project/yusuzech/r-web-scraping-cheat-sheet">
    <title>R Web Scraping Cheat Sheet</title>
    <dc:date>2021-03-30T12:36:10+00:00</dc:date>
    <link>https://awesomeopensource.com/project/yusuzech/r-web-scraping-cheat-sheet</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_teach:statcomp R via:phnk</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:816b5c9ee29b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:phnk"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780190900663.001.0001">
    <title>Reproducible Econometrics Using R - Oxford Scholarship</title>
    <dc:date>2021-01-16T07:03:52+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780190900663.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book is designed to facilitate reproducibility in Econometrics. It does so by using open source software (R) and recently developed tools (R Markdown and bookdown) that allow the reader to engage in reproducible research. Illustrative examples are provided throughout, and a range of topics are covered. Assignments, exams, slides, and a solution manual are available for instructors."

--- I checked out the library's codex copy just before lockdown and have been slowly, but enjoyably, going through it...]]></description>
<dc:subject>to:NB books:noted to_download to_read racine.jeffrey econometrics R to_teach:undergrad-ADA to_teach:data_over_space_and_time statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:66446d8278e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_download"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racine.jeffrey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2101.02912">
    <title>[2101.02912] Nonlinear Optimization in R using nlopt</title>
    <dc:date>2021-01-11T15:23:21+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.02912</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this article, we present a problem of nonlinear constraint optimization with equality and inequality constraints. Objective functions are defined to be nonlinear and optimizers may have a lower and upper bound. We solve the optimization problem using the open-source R package nloptr. Several examples have been presented."

--- This is essentially a tutorial in using the package, and probably about the right speed for The Kids.]]></description>
<dc:subject>to:NB R optimization to_teach:statcomp have_skimmed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4fe089aa22bf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://acerbialberto.com/publication/preprint_ibmcultevo/">
    <title>Individual-based models of cultural evolution. A step-by-step guide using R | Alberto Acerbi</title>
    <dc:date>2020-12-19T04:02:14+00:00</dc:date>
    <link>https://acerbialberto.com/publication/preprint_ibmcultevo/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The field of cultural evolution has emerged in the last few decades as a thriving, interdisciplinary effort to understand cultural change and cultural diversity within an evolutionary framework and using evolutionary tools, concepts and methods. Given its roots in evolutionary biology, much of cultural evolution is grounded in, or inspired by, formal models. Yet many researchers interested in cultural evolution come from backgrounds that lack training in formal models, such as psychology, anthropology or archaeology. The aim of this book is to partly address this gap by showing readers how to create individual-based models (IBMs, also known as agent-based models, or ABMs) of cultural evolution. We provide example code written in the programming language R, which has been widely adopted in the scientific community. We will go from very simple models of the basic processes of cultural evolution, such as biased transmission and cultural mutation, to more advanced topics such as the evolution of social learning, demographic effects, and social network analysis. Where possible we recreate existing models in the literature, so that readers can better understand those existing models, and perhaps even extend them to address questions of their own interest. Please notice this is a ‘living’ book. It will be updated over time."]]></description>
<dc:subject>agent-based_models cultural_evolution books:noted R in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:06d1267a86a0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v096i07">
    <title>fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks | Dong | Journal of Statistical Software</title>
    <dc:date>2020-12-07T01:26:32+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v096i07</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented."]]></description>
<dc:subject>to:NB to_read network_data_analysis R computational_statistics to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:50ff11af746c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v096i05">
    <title>Generalized Network Autoregressive Processes and the GNAR Package | Knight | Journal of Statistical Software</title>
    <dc:date>2020-11-30T03:00:09+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v096i05</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new class of generalized network autoregressive processes. Such processes consist of a multivariate time series along with a real, or inferred, network that provides information about inter-variable relationships. The GNAR model relates values of a time series for a given variable and time to earlier values of the same variable and of neighboring variables, with inclusion controlled by the network structure. The GNAR package is designed to fit this new model, while working with standard 'ts' objects and the igraph package for ease of use."]]></description>
<dc:subject>to:NB time_series network_data_analysis R</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2a9ed62a57b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sites.google.com/site/spiketrainanalysiswithr">
    <title>Spike Train Analysis with R</title>
    <dc:date>2020-11-29T20:19:46+00:00</dc:date>
    <link>https://sites.google.com/site/spiketrainanalysiswithr</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R neural_data_analysis neural_coding_and_decoding point_processes time_series to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bbc278b02741/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_coding_and_decoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kieranhealy.org/blog/archives/2020/04/10/covdata-package/">
    <title>Covdata Package - kieranhealy.org</title>
    <dc:date>2020-04-16T23:40:56+00:00</dc:date>
    <link>https://kieranhealy.org/blog/archives/2020/04/10/covdata-package/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>coronavirus_pandemic_of_2019-- R to_teach:data_over_space_and_time healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5789ef366fd1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coronavirus_pandemic_of_2019--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v054i02">
    <title>adabag: An R Package for Classification with Boosting and Bagging | Alfaro | Journal of Statistical Software</title>
    <dc:date>2019-12-01T15:47:34+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v054i02</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting) is the best known, although it is suitable only for dichotomous tasks. AdaBoost.M1 and SAMME (stagewise additive modeling using a multi-class exponential loss function) are two easy and natural extensions to the general case of two or more classes. In this paper, the adabag R package is introduced. This version implements AdaBoost.M1, SAMME and bagging algorithms with classification trees as base classifiers. Once the ensembles have been trained, they can be used to predict the class of new samples. The accuracy of these classifiers can be estimated in a separated data set or through cross validation. Moreover, the evolution of the error as the ensemble grows can be analysed and the ensemble can be pruned. In addition, the margin in the class prediction and the probability of each class for the observations can be calculated. Finally, several classic examples in classification literature are shown to illustrate the use of this package."]]></description>
<dc:subject>to:NB boosting bagging ensemble_methods classifiers decision_trees R to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:af00024c2969/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:boosting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bagging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kjhealy.github.io/gssr/articles/overview.html">
    <title>An Overview of gssr • gssr</title>
    <dc:date>2019-10-11T00:41:35+00:00</dc:date>
    <link>https://kjhealy.github.io/gssr/articles/overview.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The General Social Survey, or GSS, is one of the cornerstones of American social science and one of the most-analyzed datasets in Sociology. It is routinely used in research, in teaching, and as a reference point in discussions about changes in American society since the early 1970s. It is also a model of open, public data. The National Opinion Research Center already provides many excellent tools for working with the data, and has long made it freely available to researchers. Casual users of the GSS can examine the GSS Data Explorer, and social scientists can download complete datasets directly. At present, the GSS is provided to researchers in a choice of two commercial formats, Stata (.dta) and SPSS (.sav). It’s not too difficult to get the data into R (especially now that the Haven package is pretty reliable), but it can be a little annoying to have to do it repeatedly. After doing it one too many times, I got tired of it and I made a package instead. The gssr package provides the GSS Cumulative Data File (1972-2018) and the GSS Three Wave Panel Data File (2006-2010), together with their codebooks, in a format that makes it straightforward to get started working with them in R. The gssr package makes the GSS a little more accessible to users of R, the free software environment for statistical computing, and thus helps in a small way to make the GSS even more open than it already is"]]></description>
<dc:subject>to_explore sociology R data_sets healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1b102e610ff8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_explore"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.03813">
    <title>[1909.03813] INTEREST: INteractive Tool for Exploring REsults from Simulation sTudies</title>
    <dc:date>2019-09-18T13:03:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.03813</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Simulation studies allow us to explore the properties of statistical methods. They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. It is crucial that we can digest relevant results of simulation studies. Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. Consequently, the reader can focus on simulation parameters and estimands of most interest. In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely."]]></description>
<dc:subject>to:NB simulation R to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b85f6a61ce88/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06936">
    <title>[1908.06936] ExaGeoStatR: A Package for Large-Scale Geostatistics in R</title>
    <dc:date>2019-08-20T14:06:56+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06936</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Parallel computing in Gaussian process calculation becomes a necessity for avoiding computational and memory restrictions associated with Geostatistics applications. The evaluation of the Gaussian log-likelihood function requires O(n^2) storage and O(n^3) operations where n is the number of geographical locations. In this paper, we present ExaGeoStatR, a package for large-scale Geostatistics in R that supports parallel computation of the maximum likelihood function on shared memory, GPU, and distributed systems. The parallelization depends on breaking down the numerical linear algebra operations into a set of tasks and rendering them for a task-based programming model. ExaGeoStatR supports several maximum likelihood computation variants such as exact, Diagonal Super Tile (DST), and Tile Low-Rank (TLR) approximation besides providing a tool to generate large-scale synthetic datasets which can be used to test and compare different approximations methods. The package can be used directly through the R environment without any C, CUDA, or MPIknowledge. Here, we demonstrate the ExaGeoStatR package by illustrating its implementation details, analyzing its performance on various parallel architectures, and assessing its accuracy using both synthetic datasets and a sea surface temperature dataset. The performance evaluation involves spatial datasets with up to 250K observations."]]></description>
<dc:subject>to:NB spatial_statistics prediction computational_statistics R statistics to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:befa53ca1970/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://dahtah.github.io/imager/imager.html">
    <title>imager: an R package for image processing</title>
    <dc:date>2019-08-09T20:21:02+00:00</dc:date>
    <link>https://dahtah.github.io/imager/imager.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>graphics R to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:96f6fe9717d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cran.r-project.org/web/packages/magick/vignettes/intro.html">
    <title>The magick package: Advanced Image-Processing in R</title>
    <dc:date>2019-08-09T20:20:38+00:00</dc:date>
    <link>https://cran.r-project.org/web/packages/magick/vignettes/intro.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>graphics R to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:884220760f7c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.02101">
    <title>[1904.02101] The Landscape of R Packages for Automated Exploratory Data Analysis</title>
    <dc:date>2019-08-01T13:44:55+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.02101</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. "
There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of twelve popular R packages to identify the parts of analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development.]]></description>
<dc:subject>to:NB R exploratory_data_analysis data_analysis statistics to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:704bfbb81807/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:exploratory_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://global.oup.com/academic/product/evolutionary-genetics-9780198830917?cc=us&amp;lang=en#">
    <title>Evolutionary Genetics - Hardcover - Glenn-Peter Saetre; Mark Ravinet - Oxford University Press</title>
    <dc:date>2019-07-24T14:15:51+00:00</dc:date>
    <link>https://global.oup.com/academic/product/evolutionary-genetics-9780198830917?cc=us&amp;lang=en#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["With recent technological advances, vast quantities of genetic and genomic data are being generated at an ever-increasing pace. The explosion in access to data has transformed the field of evolutionary genetics. A thorough understanding of evolutionary principles is essential for making sense of this, but new skill sets are also needed to handle and analyze big data. This contemporary textbook covers all the major components of modern evolutionary genetics, carefully explaining fundamental processes such as mutation, natural selection, genetic drift, and speciation. It also draws on a rich literature of exciting and inspiring examples to demonstrate the diversity of evolutionary research, including an emphasis on how evolution and selection has shaped our own species. 
"Practical experience is essential for developing an understanding of how to use genetic and genomic data to analyze and interpret results in meaningful ways. In addition to the main text, a series of online tutorials using the R language serves as an introduction to programming, statistics, and analysis. Indeed the R environment stands out as an ideal all-purpose source platform to handle and analyze such data. The book and its online materials take full advantage of the authors' own experience in working in a post-genomic revolution world, and introduces readers to the plethora of molecular and analytical methods that have only recently become available. 
"Evolutionary Genetics is an advanced but accessible textbook aimed principally at students of various levels (from undergraduate to postgraduate) but also for researchers looking for an updated introduction to modern evolutionary biology and genetics. "]]></description>
<dc:subject>to:NB genetics evolutionary_biology statistics R books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b2bc7d74e413/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1629942">
    <title>Scalable Visualization Methods for Modern Generalized Additive Models: Journal of Computational and Graphical Statistics: Vol 0, No 0</title>
    <dc:date>2019-07-24T14:15:06+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1629942</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualization tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that (a) are fast enough for interactive use, (b) exploit the additive structure of GAMs, (c) scale to large data sets, and (d) can be used in conjunction with a wide range of response distributions. The new visual methods proposed here are implemented by the mgcViz R package, available on the Comprehensive R Archive Network. Supplementary materials for this article are available online."]]></description>
<dc:subject>to:NB additive_models visual_display_of_quantitative_information computational_statistics statistics R to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4954db6bc43a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:additive_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1703.04467">
    <title>[1703.04467] spmoran: An R package for Moran's eigenvector-based spatial regression analysis</title>
    <dc:date>2019-07-24T13:16:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.04467</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This study illustrates how to use "spmoran," which is an R package for Moran's eigenvector-based spatial regression analysis for up to millions of observations. This package estimates fixed or random effects eigenvector spatial filtering models and their extensions including a spatially varying coefficient model, a spatial unconditional quantile regression model, and low rank spatial econometric models. These models are estimated computationally efficiently."

--- ETA after reading: The approach sounds interesting enough that I want to track down the references that actually explain it, rather than just the software.]]></description>
<dc:subject>spatial_statistics regression statistics to_teach:data_over_space_and_time R have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:15853f0841ac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kieranhealy.org/blog/archives/2019/03/22/a-quick-and-tidy-look-at-the-2018-gss/">
    <title>A Quick and Tidy Look at the 2018 GSS</title>
    <dc:date>2019-03-26T22:30:09+00:00</dc:date>
    <link>https://kieranhealy.org/blog/archives/2019/03/22/a-quick-and-tidy-look-at-the-2018-gss/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Where by "to_teach" I mean "to work through myself".]]></description>
<dc:subject>R visual_display_of_quantitative_information to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2d414ea6cf1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/9781108705295">
    <title>Modern statistics modern biology | Statistics for life sciences, medicine and health | Cambridge University Press</title>
    <dc:date>2019-02-25T20:24:16+00:00</dc:date>
    <link>https://www.cambridge.org/9781108705295</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you 'cooking from scratch', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code."]]></description>
<dc:subject>to:NB books:noted statistics computational_statistics biology genomics R</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bf9e685bd5b4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/book/10.1002/9781119991595">
    <title>How to be a Quantitative Ecologist | Wiley Online Books</title>
    <dc:date>2019-01-07T18:37:04+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/book/10.1002/9781119991595</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Ecological research is becoming increasingly quantitative, yet students often opt out of courses in mathematics and statistics, unwittingly limiting their ability to carry out research in the future. This textbook provides a practical introduction to quantitative ecology for students and practitioners who have realised that they need this opportunity.
"The text is addressed to readers who haven't used mathematics since school, who were perhaps more confused than enlightened by their undergraduate lectures in statistics and who have never used a computer for much more than word processing and data entry. From this starting point, it slowly but surely instils an understanding of mathematics, statistics and programming, sufficient for initiating research in ecology. The book’s practical value is enhanced by extensive use of biological examples and the computer language R for graphics, programming and data analysis."]]></description>
<dc:subject>to:NB books:noted downloaded ecology statistics R</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:216dd73313d0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/an-introduction-to-the-advanced-theory-and-practice-of-nonparametric-econometrics/974161A820CE022349B95AF2320C25FA">
    <title>An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics by Jeffrey S. Racine</title>
    <dc:date>2019-01-06T02:05:38+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/an-introduction-to-the-advanced-theory-and-practice-of-nonparametric-econometrics/974161A820CE022349B95AF2320C25FA</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Interest in nonparametric methodology has grown considerably over the past few decades, stemming in part from vast improvements in computer hardware and the availability of new software that allows practitioners to take full advantage of these numerically intensive methods. This book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine's earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice (2007). The open source R platform for statistical computing and graphics is used throughout in conjunction with the R package np. Recent developments in reproducible research is emphasized throughout with appendices devoted to helping the reader get up to speed with R, R Markdown, TeX and Git."

--- Ooh.]]></description>
<dc:subject>to:NB books:noted econometrics nonparametrics statistics racine.jeffrey R coveted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85a96986fe7c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racine.jeffrey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coveted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.springer.com/gp/book/9783319555676">
    <title>Simulation and Inference for Stochastic Processes with YUIMA - A Comprehensive R Framework for SDEs and Other Stochastic Processes | Stefano M. Iacus | Springer</title>
    <dc:date>2019-01-05T05:54:34+00:00</dc:date>
    <link>https://www.springer.com/gp/book/9783319555676</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB books:noted stochastic_processes statistical_inference_for_stochastic_processes R to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2fd03805ac90/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/titles/13268.html">
    <title>Allesina, S. and Wilmes, M.: Computing Skills for Biologists: A Toolbox (Hardcover, Paperback and eBook) | Princeton University Press</title>
    <dc:date>2019-01-05T04:21:56+00:00</dc:date>
    <link>https://press.princeton.edu/titles/13268.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["While biological data continues to grow exponentially in size and quality, many of today’s biologists are not trained adequately in the computing skills necessary for leveraging this information deluge. In Computing Skills for Biologists, Stefano Allesina and Madlen Wilmes present a valuable toolbox for the effective analysis of biological data.
"Based on the authors’ experiences teaching scientific computing at the University of Chicago, this textbook emphasizes the automation of repetitive tasks and the construction of pipelines for data organization, analysis, visualization, and publication. Stressing practice rather than theory, the book’s examples and exercises are drawn from actual biological data and solve cogent problems spanning the entire breadth of biological disciplines, including ecology, genetics, microbiology, and molecular biology. Beginners will benefit from the many examples explained step-by-step, while more seasoned researchers will learn how to combine tools to make biological data analysis robust and reproducible. The book uses free software and code that can be run on any platform.
"Computing Skills for Biologists is ideal for scientists wanting to improve their technical skills and instructors looking to teach the main computing tools essential for biology research in the twenty-first century."]]></description>
<dc:subject>to:NB books:noted scientific_computing R to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:73f804805d40/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:scientific_computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://statsthinking21.org/">
    <title>Statistical Thinking for the 21st Century</title>
    <dc:date>2018-12-13T19:47:56+00:00</dc:date>
    <link>http://statsthinking21.org/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:noted poldrack.russell statistics R</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b7353d35f111/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:poldrack.russell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/">
    <title>Confidence intervals for GLMs</title>
    <dc:date>2018-12-11T13:28:10+00:00</dc:date>
    <link>https://www.fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[For the trick about finding the inverse link function.]]></description>
<dc:subject>regression R to_teach:undergrad-ADA via:kjhealy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ecc65569c27d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v033i09">
    <title>Solving Differential Equations in R: Package deSolve | Soetaert | Journal of Statistical Software</title>
    <dc:date>2018-12-07T00:17:07+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v033i09</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we present the R package deSolve to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines approach. The differential equations can be represented in R code or as compiled code. In the latter case, R is used as a tool to trigger the integration and post-process the results, which facilitates model development and application, whilst the compiled code significantly increases simulation speed. The methods implemented are efficient, robust, and well documented public-domain Fortran routines. They include four integrators from the ODEPACK package (LSODE, LSODES, LSODA, LSODAR), DVODE and DASPK2.0. In addition, a suite of Runge-Kutta integrators and special-purpose solvers to efficiently integrate 1-, 2- and 3-dimensional partial differential equations are available. The routines solve both stiff and non-stiff systems, and include many options, e.g., to deal in an efficient way with the sparsity of the Jacobian matrix, or finding the root of equations. In this article, our objectives are threefold: (1) to demonstrate the potential of using R for dynamic modeling, (2) to highlight typical uses of the different methods implemented and (3) to compare the performance of models specified in R code and in compiled code for a number of test cases. These comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables. Nevertheless, the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code. Still, amongst the benefits of R are a more flexible and interactive implementation, better readability of the code, and access to R’s high-level procedures. deSolve is the successor of package odesolve which will be deprecated in the future; it is free software and distributed under the GNU General Public License, as part of the R software project."]]></description>
<dc:subject>to:NB dynamical_systems computational_statistics R to_teach:data_over_space_and_time have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:42e512bf627c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v016i09">
    <title>Object-oriented Computation of Sandwich Estimators | Zeileis | Journal of Statistical Software</title>
    <dc:date>2018-10-26T04:32:52+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v016i09</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions' most importantly for the empirical estimating functions' from which various types of sandwich estimators can be computed."
]]></description>
<dc:subject>to:NB computational_statistics R estimation regression statistics to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e85d23b7b4f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cran.r-project.org/web/packages/rWind/rWind.pdf">
    <title>rWind package on CRAN</title>
    <dc:date>2018-10-04T06:24:22+00:00</dc:date>
    <link>https://cran.r-project.org/web/packages/rWind/rWind.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[For access to wind velocity data sets.  (Surprisingly slow access, but very glad somebody has written this so I don't have to!)

--- ETA: The server they're yanking the data from is very temperamental, and grabbing a long temporal stretch is almost sure to fail.  But grabbing about 30 days of data at a time seems OK.]]></description>
<dc:subject>R data_sets to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:42a87a67f055/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://notstatschat.rbind.io/2018/09/27/how-to-write-a-racist-ai-in-r-without-really-trying/">
    <title>How to write a racist AI in R without really trying - Biased and Inefficient</title>
    <dc:date>2018-09-28T12:10:56+00:00</dc:date>
    <link>https://notstatschat.rbind.io/2018/09/27/how-to-write-a-racist-ai-in-r-without-really-trying/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>text_mining R racism to_teach:data-mining to_teach:statcomp to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b83367edb66a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:text_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ggdag.netlify.com/">
    <title>Analyze and Create Elegant Directed Acyclic Graphs • ggdag</title>
    <dc:date>2018-08-09T18:28:25+00:00</dc:date>
    <link>https://ggdag.netlify.com/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["ggdag: An R Package for visualizing and analyzing directed acyclic graphs"]]></description>
<dc:subject>R graphical_models visual_display_of_quantitative_information via:arsyed to_teach:undergrad-ADA re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3545bb27c7de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.stat.washington.edu/peter/591/labs/Lab4/Lab4.SpatioTemporal.html">
    <title>Modeling using the SpatioTemporal R package</title>
    <dc:date>2018-08-07T15:46:44+00:00</dc:date>
    <link>https://www.stat.washington.edu/peter/591/labs/Lab4/Lab4.SpatioTemporal.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R spatio-temporal_statistics prediction to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d13eff7900e3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.08105">
    <title>[1705.08105] FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets</title>
    <dc:date>2018-08-07T15:44:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.08105</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["FRK is an R software package for spatial/spatio-temporal modelling and prediction with large datasets. It facilitates optimal spatial prediction (kriging) on the most commonly used manifolds (in Euclidean space and on the surface of the sphere), for both spatial and spatio-temporal fields. It differs from many of the packages for spatial modelling and prediction by avoiding stationary and isotropic covariance and variogram models, instead constructing a spatial random effects (SRE) model on a fine-resolution discretised spatial domain. The discrete element is known as a basic areal unit (BAU), whose introduction in the software leads to several practical advantages. The software can be used to (i) integrate multiple observations with different supports with relative ease; (ii) obtain exact predictions at millions of prediction locations (without conditional simulation); and (iii) distinguish between measurement error and fine-scale variation at the resolution of the BAU, thereby allowing for reliable uncertainty quantification. The temporal component is included by adding another dimension. A key component of the SRE model is the specification of spatial or spatio-temporal basis functions; in the package, they can be generated automatically or by the user. The package also offers automatic BAU construction, an expectation-maximisation (EM) algorithm for parameter estimation, and functionality for prediction over any user-specified polygons or BAUs. Use of the package is illustrated on several spatial and spatio-temporal datasets, and its predictions and the model it implements are extensively compared to others commonly used for spatial prediction and modelling."]]></description>
<dc:subject>to_read R heard_the_talk prediction spatial_statistics spatio-temporal_statistics to_teach:data_over_space_and_time in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:31e79e2da0ae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cran.r-project.org/web/views/SpatioTemporal.html">
    <title>CRAN Task View: Handling and Analyzing Spatio-Temporal Data</title>
    <dc:date>2018-08-07T15:43:46+00:00</dc:date>
    <link>https://cran.r-project.org/web/views/SpatioTemporal.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R computational_statistics spatio-temporal_statistics to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bc9abb4bafd5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://tidymodels.github.io/rsample/">
    <title>General Resampling Infrastructure • rsample</title>
    <dc:date>2018-08-03T14:21:45+00:00</dc:date>
    <link>https://tidymodels.github.io/rsample/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["rsample contains a set of functions that can create different types of resamples and corresponding classes for their analysis. The goal is to have a modular set of methods that can be used across different R packages for:
"traditional resampling techniques for estimating the sampling distribution of a statistic and
"estimating model performance using a holdout set
"The scope of rsample is to provide the basic building blocks for creating and analyzing resamples of a data set but does not include code for modeling or calculating statistics. The “Working with Resample Sets” vignette gives demonstrations of how rsample tools can be used."]]></description>
<dc:subject>to:NB R computational_statistics to_teach:statcomp to_teach:undergrad-ADA via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1501cde525fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tidyverse.org/articles/2017/12/workflow-vs-script/">
    <title>Project-oriented workflow - Tidyverse</title>
    <dc:date>2018-07-07T15:57:32+00:00</dc:date>
    <link>https://www.tidyverse.org/articles/2017/12/workflow-vs-script/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0d05276f78d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://bookdown.org/yihui/blogdown/">
    <title>blogdown: Creating Websites with R Markdown</title>
    <dc:date>2018-06-06T16:37:52+00:00</dc:date>
    <link>https://bookdown.org/yihui/blogdown/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Since I am, as a loyal reader informs me, the last human being still using Blosxom...]]></description>
<dc:subject>R to_read blogging</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8b81157825d3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogging"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cambridge.org/9781107655577">
    <title>Quantitative methods archaeology using R | Archaeological theory and methods | Cambridge University Press</title>
    <dc:date>2018-05-30T12:54:34+00:00</dc:date>
    <link>http://cambridge.org/9781107655577</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity."

--- This looks like it might be an interesting source of teaching examples.  (OTOH, I'm not sure how many of The Kids would get it...)]]></description>
<dc:subject>to:NB books:noted archaeology statistics R books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:125f2ff23238/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:archaeology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://socviz.co/">
    <title>Data Visualization for Social Science</title>
    <dc:date>2017-09-13T21:46:38+00:00</dc:date>
    <link>http://socviz.co/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Publisher's website for the book:
https://press.princeton.edu/titles/13826.html]]></description>
<dc:subject>visual_display_of_quantitative_information R healy.kieran to_teach:undergrad-research books:owned have_read books:recommended in_NB kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aaf89c535ccf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://docs.renjin.org/en/latest/package/index.html">
    <title>4. Using Renjin as an R Package — Renjin 0.8.2423 documentation</title>
    <dc:date>2017-09-11T14:24:11+00:00</dc:date>
    <link>http://docs.renjin.org/en/latest/package/index.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39db603f3239/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://journal.sjdm.org/17/17217/jdm17217.html">
    <title>FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees</title>
    <dc:date>2017-08-01T14:47:49+00:00</dc:date>
    <link>http://journal.sjdm.org/17/17217/jdm17217.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use."

--- I am skeptical about that "simple enough for anyone to understand and use"]]></description>
<dc:subject>decision_trees heuristics cognitive_science R have_read to_teach:undergrad-ADA re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc94e08f69e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://shiny.stat.cmu.edu:3838/hseltman/IROL/">
    <title>Interactive R On-Line</title>
    <dc:date>2016-08-30T14:47:38+00:00</dc:date>
    <link>http://shiny.stat.cmu.edu:3838/hseltman/IROL/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["IROL was developed by the team of Howard Seltman (email feedback), Rebecca Nugent, Sam Ventura, Ryan Tibshirani, and Chris Genovese at the Department of Statistics at Carnegie Mellon University."

--- I mark this as "to_teach:statcomp", but of course the point is to have people go through this _before_ that course, so the class can cover more interesting stuff.]]></description>
<dc:subject>R kith_and_kin seltman.howard nugent.rebecca genovese.christopher ventura.samuel tibshirani.ryan to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2f008cb8a411/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:seltman.howard"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nugent.rebecca"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genovese.christopher"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ventura.samuel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tibshirani.ryan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.cmu.edu/~hseltman/shinyTex/">
    <title>shinyTex</title>
    <dc:date>2016-08-30T14:45:47+00:00</dc:date>
    <link>http://www.stat.cmu.edu/~hseltman/shinyTex/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["ShinyTex is a system for authoring interactive World Wide Web applications (apps) which includes the full capabilities of the R statistical language, particularly in the context of Technology Enhanced Learning (TEL). It uses a modified version of the LaTeX syntax that is standard for document creation among mathematicians and statisticians. It is built on the Shiny platform, an extension of R designed by RStudio to produce web apps. The goal is to provide an easy to use TEL authoring environment with excellent mathematical and statistical support using only free software. ShinyTex authoring can be performed on Windows, OS X, and Linux. Users may view the app on any system with a standard web browser."]]></description>
<dc:subject>R latex kith_and_kin seltman.howard</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f8e248d62af3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latex"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:seltman.howard"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/book/10.1007%2F978-3-319-23883-8">
    <title>A User’s Guide to Network Analysis in R - Springer</title>
    <dc:date>2016-08-30T13:20:05+00:00</dc:date>
    <link>http://link.springer.com/book/10.1007%2F978-3-319-23883-8</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Apparently covers both igraph and statnet.]]></description>
<dc:subject>to:NB books:noted R network_data_analysis to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:459c2f7c2cf1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://stat545-ubc.github.io/bit007_draw-the-rest-of-the-owl.html">
    <title>Draw the rest of the owl</title>
    <dc:date>2016-03-29T17:10:35+00:00</dc:date>
    <link>http://stat545-ubc.github.io/bit007_draw-the-rest-of-the-owl.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This strikes me as really excellent pedagogy.]]></description>
<dc:subject>R programming to_teach:statcomp problem-solving bryan.jennifer via:tslumley</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1387c2d9f921/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:problem-solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bryan.jennifer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:tslumley"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/JennyBryan/status/704779515558400000">
    <title>Jenny Bryan on Twitter: &quot;An Incomplete List of #rstats troubleshooting tips https://t.co/OKKoGkSYzq&quot;</title>
    <dc:date>2016-03-01T23:52:23+00:00</dc:date>
    <link>https://twitter.com/JennyBryan/status/704779515558400000</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[It misses
* Did you use attach()? Don't
but is otherwise pretty good.]]></description>
<dc:subject>R to_teach:undergrad-ADA to_teach:statcomp via:tslumley bryan.jennifer</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c10b66a6e7d6/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:tslumley"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bryan.jennifer"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mosaic-web.org/go/StatisticalModeling/">
    <title>Statistical Modeling: A Fresh Approach</title>
    <dc:date>2015-12-29T17:25:58+00:00</dc:date>
    <link>http://www.mosaic-web.org/go/StatisticalModeling/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Statistical Modeling: A Fresh Approach introduces and illuminates the statistical reasoning used in modern research throughout the natural and social sciences, medicine, government, and commerce. It emphasizes the use of models to untangle and quantify variation in observed data. By a deft and concise use of computing coupled with an innovative geometrical presentation of the relationship among variables, A Fresh Approach reveals the logic of statistical inference and empowers the reader to use and understand techniques such as analysis of covariance that appear widely in published research but are hardly ever found in introductory texts.
"Recognizing the essential role the computer plays in modern statistics, A Fresh Approach provides a complete and self-contained introduction to statistical computing using the powerful (and free) statistics package R."]]></description>
<dc:subject>in_NB books:noted statistics regression R re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4246fbc5a822/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
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