"Risadynamiclanguageforstatisticalcomputingthatcombineslazy functional features and object-oriented programming. This rather unlikely lin- guistic cocktail would probably never have been prepared by computer scientists, yet the language has become surprisingly popular. With millions of lines of R code available in repositories, we have an opportunity to evaluate the fundamental choices underlying the R language design. Using a combination of static and dynamic program analysis we assess the success of different language features."

There's something a bit odd about evaluating a language designed for statistical computing on a set of benchmarks which do not include statistical problems...

"This article describes the benchden package which implements a set of 28 example densities for nonparametric density estimation in R. In addition to the usual functions that evaluate the density, distribution and quantile functions or generate random variates, a function designed to be specifically useful for larger simulation studies has been added. After describing the set of densities and the usage of the package, a small toy example of a simulation study conducted using the benchden package is given."

"Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences.  Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years.  In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software.  This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages.  In addition, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R.  Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data."

"We present a new computing environment for authoring mixed natural and computer language documents. In this environment a single hierarchically-organized plain text source file may contain a variety of elements such as code in arbitrary programming languages, raw data, links to external resources, project management data, working notes, and text for publication. Code fragments may be executed in situ with graphical, numerical and textual output captured or linked in the file. Export to LATEX, HTML, LATEX beamer, DocBook and other formats permits working reports, presentations and manuscripts for publication to be generated from the file. In addition, functioning pure code files can be automatically extracted from the file. This environment is implemented as an extension to the Emacs text editor and provides a rich set of features for authoring both prose and code, as well as sophisticated project management capabilities."

"Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques."

--- This might be a good book for a baby stats. class; but even if it's a _great_ book, how on Earth am I supposed to justify asking students to spend $130 for it?

Review: http://cscs.umich.edu/~crshalizi/weblog/algae-2011-12.html#matloff

The R Graph Gallery is an under-utilized resource, and sending a little money Romain's way is not a bad thing.

Extracting data from your

"The US Decennial Census is arguably the most important data set for social science research in the United States. The UScensus2000 suite of packages allows for convenient handling of the 2000 US Census spatial and demographic data. The goal of this article is to showcase the UScensus2000 suite of packages for R, to describe the data contained within these packages, and to demonstrate the helper functions provided for handling this data. The UScensus2000 suite is comprised of spatial and demographic data for the 50 states and Washington DC at four different geographic levels (block, block group, tract, and census designated place). The UScensus2000 suite also contains a number of functions for selecting and aggregating specific geographies or demographic information such as metropolitan statistical areas, counties, etc. ... This article will provide the necessary background for working with this data set, helper functions, and finish with an applied spatial statistics example."

"NetLogo is a software platform for agent-based modelling that is increasingly used in ecological and environmental modelling. So far, for comprehensive analyses of agent-based models (ABMs) implemented in NetLogo, results needed to be written to files and evaluated by using external software, for example R. Ideally, however, it would be possible to call any R function from within a NetLogo program. This would allow sophisticated interactive statistical analysis of model structure and dynamics, using R functions and packages for generating certain statistical distributions and experimental design, and for implementing complex descriptive submodels within ABMs. Here we present an R extension of NetLogo. It consists of only nine new NetLogo primitives for sending data between NetLogo and R and for calling R functions (six additional primitives for debugging). We demonstrate the usage of the R extension with three short examples."

I hesitate about the teaching tag, this seems quite clunky --- but perhaps it's not that bad when you try it.

"depmixS4 implements a general framework for defining and estimating dependent mixture models in the R programming language. This includes standard Markov models, latent/hidden Markov models, and latent class and finite mixture distribution models. The models can be fitted on mixed multivariate data with distributions from the glm family, the (logistic) multinomial, or the multivariate normal distribution. Other distributions can be added easily, and an example is provided with the exgaus distribution. Parameters are estimated by the expectation-maximization (EM) algorithm or, when (linear) constraints are imposed on the parameters, by direct numerical optimization with the Rsolnp or Rdonlp2 routines."

C++ library with R interface, supposedly good for Really Big data. Consider for 350?

Here, here.

Huh.

Describes working with the standard cross-cultural sample in R. TODO: track down the actual file! TODO: think about devising suitable examples/problems for data mining.

About 1/2 R as a programming language, and 1/2 shot explanations of how to do particular analyses in R. I've now used it with decent success as a supplemental textbook.

Reasonable though not mandatory.

Diffusion maps in R. (How handy that the TA for the class wrote the package...)

"This paper presents the R package gRapHD for efficient selection of high-dimensional undirected graphical models. The package provides tools for selecting trees, forests and decomposable models minimizing information criteria such as AIC or BIC, and for displaying the independence graphs of the models. It has also some useful tools for analysing graphical structures. It supports the use of discrete, continuous, or both types of variables."

To read; possibly useful for the end of 350 when I want to talk about causality?

We should consider distributing this to the incoming graduate students. (Except we'd need to make it clear that using Word is NOT ACCEPTABLE.)

"Sweave is a tool that allows to embed the R code for complete data analyses in latex documents. The purpose is to create dynamic reports, which can be updated automatically if data or analysis change. Instead of inserting a prefabricated graph or table into the report, the master document contains the R code necessary to obtain it. When run through R, all data analysis output (tables, graphs, etc.) is created on the fly and inserted into a final latex document. The report can be automatically updated if data or analysis change, which allows for truly reproducible research."

"The ReadMe software package for R takes as input a set of text documents (such as speeches, blog posts, newspaper articles, judicial opinions, movie reviews, etc.), a categorization scheme chosen by the user (e.g., ordered positive to negative sentiment ratings, unordered policy topics, or any other mutually exclusive and exhaustive set of categories), and a small subset of text documents hand classified into the given categories. If used properly, ReadMe will report, normally within sampling error of the truth, the proportion of documents within each of the given categories among those not hand coded. ReadMe computes quantities of interest to the scientific community based on the distribution within categories but does so by skipping the more error prone intermediate step of classifing individual documents. Other procedures are also included to make processing text easy."

Very reasonable set of slides from Thomas Lumley. I wouldn't plan on actually using them in a course --- they don't quite fit my style --- but I would put them on a list of pointers for students.

Holy shit.

State-space modeling with linear/Gaussian state evolution and generalized linear models for the observations. Looks reasonable, lacks a few improvements like diffuse initial conditions in the Kalman filter.

"This package provides a bidirectional interface for calling R from Perl and Perl from R."

Online HTML R documentation. Doesn't cover all the packages, but handy for linking to the basics.

"If you are using R and you think you’re in hell, this is a map for you. "

Nice-looking graphics system for R; draft book and R package.

Non- and semi- parametric kernel-estimation goodness. Definitely using this next time I teach data-mining.

Cute: parameterize the variance and then just do a bigger MLE.

Well-designed introduction to R for people who know the usual commercial statistical packages.

R package for implementing relative distribution methods