Two hundred thirty new packages made it to CRAN in January. Below are my “Top 40” selections (AlleleShift, autoharp, autoMrP, autostsm, aweSOM, bayesforecast, cachem, circularEV, cmprskcoxmsm, coder, dataquieR, eList, GenomeAdmixR, ggmulti, ggOceanMaps, ghcm, gplite, igoR, LPDynR, LSMRealOptions, Microsoft365R, MOSS, multibridge, NHSDataDictionaRy, OTrecod, pacviz, parallelPlot, partR2, pwt10, RandomForestsGLS, rgee, rtables, SAMtool, spNetwork, targets, thematic, torchaudio, trainR, ubms, and vimpclust) in ten categories: Data, Finance, Genomics, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities, and Visualization.

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Constraint programming is a paradigm for solving combinatorial problems that draws on a wide range of techniques from artificial intelligence, computer science, and operations research. MiniZinc is a free and open-source constraint modeling language designed for formulating constraint satisfaction and discrete optimization problems. Models are compiled into an intermediate representation that is understood by a wide range of solvers.

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The videos from the rstudio::global conference are now available online. I believe that you will find the content of most of these talks to be nothing less than compelling. The themes and moods of the talks range from informative and deeply technical R issues to data science, journalism, art, visualization, education and public service. In this post, I profile five talks that I found particularly compelling and provide a few thoughts on their significance.

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One hundred twenty-three new packages made it to CRAN in December. Here are my “Top 40” selections in nine categories: Computational Methods, Data, Genomics, Machine Learning, Medicine, Science, Statistics, Utilities, and Visualization.

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Structural Equation Models (SEM) which are common in many economic modeling efforts, require fitting and simulating whole system of equations where each equation may depend on the results of other equations. In this post, we will show how to do structural equation modeling in R by working through the Klein Model of the United States economy, one of the oldest and most elementary models of its kind.

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This post takes a closer look at the forest plot that was mentioned in a previous post introducing PSI’s Wonderful Wednesdays events. It describes a custom version of a forest plot with additional bands to visualize heterogeneity between studies in a meta-analysis that was part of a project submitted to the Wonderful Wednesdays challenge hosted by PSI and reviewed by statisticians in the organization.

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For almost a year now, the PSI Visualization Special Interest Group (VIS SIG) has been conducting a monthly graduate-level seminar on creating effective statistical visualizations that is open to everyone. Wonderful Wednesdays is a unique collegial event. Every month the SIG publishes a link to a new data set and issues a challenge to produce visualizations that effectively illustrate some specific aspects of the data. Anyone can submit an entry coded in the language of their choice.

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The world seems to have moved to a new phase of paying attention to COVID-19. We have gone from pondering daily plots of case counts, to puzzling through models and forecasts, and are now moving on to the vaccines and the science behind them. For data scientists, however, the focus needs to remain on the data and the myriad issues and challenges that efforts to collect and curate COVID data have uncovered.

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Two hundred ninety-two new packages made it to CRAN in November. Picking forty was unusually difficult. Nevertheless, here are my “Top 40” selections in twelve categories: Archaeology, Computational Methods, Data, Epidemiology, Games, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities, and Visualization. R developers continue to extend the reach of R. November featured a new package on Archaeology, one of only seventeen I could find on CRAN pkgsearch::pkg_search(query="Archaeology ",size=200), as well as a package that wraps Python’s chess package.

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This post takes a look at some basic R tools for producing eye catching three dimensional plots of surfaces and probability distributions. It showcases some interactive JavaScript enabled interactive plots along with classsic visualizations built on base R graphics.

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