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(This article was first published on <strong><a href="https://rviews.rstudio.com/2018/04/30/march-2018-top-40-new-package-picks/"> R Views</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>By my count, just over 200 new packages made it to CRAN and stuck during March. The trend for specialized, and sometimes downright esoteric science packages continues. I counted 40 new packages in this class. Most, but not all of these, are focused on bio-science applications. For example, the <a rel="nofollow" href="https://cran.r-project.org/package=foreSIGHT" target="_blank">foreSIGHT</a> package profiled below focuses on climate science. I was also pleased to see two new packages (not from RStudio) in the Data Science category, <a rel="nofollow" href="https://cran.r-project.org/package=h2o4gpu" target="_blank">h2o4gpu</a> and <a rel="nofollow" href="https://cran.r-project.org/package=onnx" target="_blank">onnx</a>, built on the <a rel="nofollow" href="https://cran.r-project.org/package=reticulate" target="_blank">reticulate</a> package for interfacing with <code>Python</code>. I hope this also becomes a trend.</p>
<p>The following are my <strong>“Top 40”</strong> picks for March in nine categories: Computational Methods, Data, Data Science, Political Science, Science, Statistics, Time Series, Utilities and Visualizations.</p>
<h3>Computational Methods</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dynprog" target="_blank">dynprog</a> v0.1.0: Implements a domain-specific language for specifying translating recursions into <a rel="nofollow" href="https://en.wikipedia.org/wiki/Dynamic_programming" target="_blank">dynamic-programming</a> algorithms.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fmlogcondens" target="_blank">fmlogcondens</a> v1.0.2: Implements a fast solver for the maximum likelihood estimator of the family of multivariate log-concave probability function. Includes well-known parametric densities including the normal, uniform, and exponential distributions and many more. For details, see <a rel="nofollow" href="doi:10.1515/auom-2015-0053" target="_blank">Rathke et al. (2015)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/fmlogcondens/vignettes/documentation.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/fmlogcondens.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=knor" target="_blank">knor</a> v0.0-5: Provides access to <a rel="nofollow" href="https://arxiv.org/pdf/1606.08905.pdf" target="_blank"><code>knor</code></a>, a NUMA-optimized, in-memory, distributed library for computing k-means.</p>
<h3>Data</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=daymetr" target="_blank">daymetr</a> v1.3.1: Provides programmatic interface to the <a rel="nofollow" href="http://daymet.ornl.gov/" target="_blank">Daymet</a> climate data. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/daymetr/vignettes/daymetr-vignette.html" target="_blank">vignette</a> shows how to use it.</p>
<p><img alt="" src="https://rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/daymetr"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=NOAAWeather" target="_blank">NOAAWeather</a> v0.1.0: Provides functions to retrieve real-time weather data from all <a rel="nofollow" href="http://www.noaa.gov/" target="_blank">NOAA</a> stations, and plot time series, boxplot, calendar heatmap, and geospatial maps to analyze trends. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/NOAAWeather/vignettes/Report.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/NOAAWeather.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ppitables" target="_blank">ppitables</a> v0.1.2: Contains country-specific lookup data tables used as reference to determine the poverty likelihood of a household based on their PPI score (<a rel="nofollow" href="https://www.povertyindex.org/" target="_blank">Poverty Probability Index</a>), with documentation from <a rel="nofollow" href="https://www.poverty-action.org/" target="_blank">Innovations for Poverty Action</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=usfertilizer" target="_blank">usfertilizer</a> v0.1.5: Provides county-level estimates of fertilizer,<br>
nitrogen and phosphorus, from 1945 to 2012 in the United States of America. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/usfertilizer/vignettes/Introduction.html" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/usfertilizer/vignettes/Data_sources_and_cleaning.html" target="_blank">Data Scources and Processes</a>.</p>
<h3>Data Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=greybox" target="_blank">greybox</a> v0.2.0: Implements tools for model selection and combinations via information criteria based on the values of partial correlations. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/greybox/vignettes/greybox.html" target="_blank">vignette</a> provides details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=h2o4gpu" target="_blank">h2o4gpu</a> v0.2.0: Implements an interface to <a rel="nofollow" href="https://github.com/h2oai/h2o4gpu" target="_blank">H2O4GPU</a>, a collection of <code>GPU</code> solvers for machine learning algorithms. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/h2o4gpu/vignettes/getting_started.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=iml" target="_blank">iml</a> v0.3.0: Provides interpretability methods to analyze the behavior and predictions of any machine learning model, including <a rel="nofollow" href="arxiv:1801.01489" target="_blank">feature importance</a>, <a rel="nofollow" href="http://www.jstor.org/stable/2699986" target="_blank">partial dependence plots</a>, [individual conditional expectation (<a rel="nofollow" href="doi:10.1080/10618600.2014.907095" target="_blank">ice plots</a>), <a rel="nofollow" href="arxiv:1602.04938" target="_blank">local models</a>, the <a rel="nofollow" href="doi:10.1007/s10115-013-0679-x" target="_blank">Shapley Value</a>, and tree surrogate models.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=iTOP" target="_blank">iTOP</a> v1.0.1: Provides functions to infer a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. The methodology is based on the extension of the <a rel="nofollow" href="doi:10.2307/2347233" target="_blank">RV coefficient</a>, a measure of matrix correlation to partial matrix correlations and binary data. See <a rel="nofollow" href="doi:10.1101/293993" target="_blank">Aben et al. (2018)</a> for details and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/iTOP/vignettes/vignette.html" target="_blank">vignette</a> introduction to the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=onnx" target="_blank">onnx</a> v0.0.1: Implements an interface to <code>ONNX</code>, the <a rel="nofollow" href="https://onnx.ai/" target="_blank">Open Neural Network Exchange</a>, which provides an open-source format for machine-learning models.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rcqp" target="_blank">rcqp</a> v0.5: Implements Corpus Query Protocol functions based on the <a rel="nofollow" href="http://cwb.sourceforge.net/" target="_blank">CWB software</a>, a collection of open-source tools for managing and querying large text corpora. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/rcqp/vignettes/rcqp.pdf" target="_blank">vignette</a> provides a roadmap.</p>
<h3>Political Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=coalitions" target="_blank">coalitions</a> v0.6.2: Implements an MCMC method to calculate probabilities for a coalition majority based on survey results. See <a rel="nofollow" href="doi:10.21105/joss.00606" target="_blank">Bender and Bauer (2018)</a>. There are vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/coalitions/vignettes/workflow.html" target="_blank">Workflows</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/coalitions/vignettes/pooling.html" target="_blank">Pooling</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/coalitions/vignettes/diagnostic.html" target="_blank">Diagnostics</a>.</p>
<h3>Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=diagmeta" target="_blank">diagmeta</a> v0.2-0: Implements methods by <a rel="nofollow" href="doi:10.1186/s12874-016-0196-1" target="_blank">Steinhauser et al. (2016)</a> for meta-analysis of diagnostic accuracy studies with several cutpoints.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=NetworkExtinction" target="_blank">NetworkExtinction</a> v0.1.0: Provides functions to simulate the extinction of species in the food web, and analyze the cascading effects as described in <a rel="nofollow" href="doi:10.1073/pnas.192407699" target="_blank">Dunne et al. (2002)</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/NetworkExtinction/vignettes/VignetteNetworkExt.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=foreSIGHT" target="_blank">foreSIGHT</a> v0.9.2: Provides a tool to create hydroclimate scenarios, stress test systems, and visualize system performance in scenario-neutral climate-change impact assessments. Functions generate perturbed time series using a range of approaches, including simple scaling of observed time series (<a rel="nofollow" href="doi:10.1002/2015WR018253" target="_blank">Culley et al. (2016)</a>) and stochastic simulation of perturbed time series. (<a rel="nofollow" href="doi:10.1016/j.jhydrol.2016.03.025" target="_blank">Guo et al. (2018)</a>). The <a rel="nofollow" href="https://cran.r-project.org/web/packages/foreSIGHT/vignettes/Vignette.html" target="_blank">vignette</a> offers a tutorial.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=PINSPlus" target="_blank">PINSPlus</a> v1.0.0: Implements <code>PINS</code>: <strong>P</strong>erturbation clustering for data <strong>IN</strong>tegration and disease <strong>S</strong>ubtyping <a rel="nofollow" href="doi:10.1101/gr.215129.116" target="_blank">Nguyen et al. (2017)</a>, a novel approach for integration of data and classification of diseases into various subtypes There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/PINSPlus/vignettes/PINSPlus.pdf" target="_blank">vignette</a>.</p>
<h3>Statistics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=chandwich" target="_blank">chandwich</a> v1.0.0: Provides functions to adjustment user-supplied independence loglikelihood functions using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in <a rel="nofollow" href="doi:10.1093/biomet/asm015" target="_blank">Chandler and Bate (2007)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/chandwich/vignettes/chandwich-vignette.html" target="_blank">vignette</a> shows how it works.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/chandwich.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ciuupi" target="_blank">ciuupi</a> v1.0.0: Provides functions to compute a confidence interval for a specified linear combination of regression parameters in a linear regression model with iid normal errors and known variance, when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. See <a rel="nofollow" href="arxiv:1708.09543" target="_blank">Kabaila and Mainzer (2017)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/ciuupi/vignettes/desciption-ciuupi.html" target="_blank">vignette</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=CoxPhLb" target="_blank">CoxPhLb</a> v1.0.0: Provides functions to analyze right-censored, length-biased data using Cox model, including model fitting and checking, and the stationarity assumption test. The model fitting and checking methods are described in <a rel="nofollow" href="doi:10.1111/j.1541-0420.2009.01287.x" target="_blank">Qin and Shen (2010)</a> and <a rel="nofollow" href="doi:10.1007/s10985-018-9422-y" target="_blank">Lee, Ning, and Shen (2018)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=cutpointr" target="_blank">cutpointr</a> v0.7.3: Provides functions to estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/cutpointr/vignettes/cutpointr.html" target="_blank">vignette</a> shows how to use the functions.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/cutpointr.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fcr" target="_blank">fcr</a> v1.0:<br>
Provides a function for dynamic prediction in functional concurrent regression that extends the <code>pffr()</code> function from the <code>refund</code> package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured. See <a rel="nofollow" href="doi:10.1002/sim.7582" target="_blank">Leroux et al. (2017)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/fcr/vignettes/dynamic-prediction.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/fcr.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ggdag" target="_blank">ggdag</a> v0.1.0: Builds on the <a rel="nofollow" href="http://dagitty.net/" target="_blank">DAGitty web tool</a> to provide functions to tidy, analyze, and plot directed acyclic graphs (DAGs). There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/ggdag/vignettes/intro-to-dags.html" target="_blank">Introduction to DAGS</a>, an <a rel="nofollow" href="https://cran.r-project.org/web/packages/ggdag/vignettes/intro-to-ggdag.html" target="_blank">Introduction to ggdag</a>, and a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/ggdag/vignettes/bias-structures.html" target="_blank">Common Structures of Bias</a>.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/ggdag.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=hdme" target="_blank">hdme</a> v0.1.1: Provides a function for penalized regression for generalized linear models for measurement error problems including the lasso (L1-penalization), which corrects for measurement error (<a rel="nofollow" href="doi:10.5705/ss.2013.180" target="_blank">Sorensen et al. (2015)</a>, and an implementation of the Generalized Matrix Uncertainty Selector (<a rel="nofollow" href="doi:10.1080/10618600.2018.1425626" target="_blank">Sorensen et al. (2018)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/hdme/vignettes/hdme-package.html" target="_blank">vignette</a> gives the details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=joineRmeta" target="_blank">joineRmeta</a> v0.1.1: Extends the joint models proposed by <a rel="nofollow" href="doi:10.1093/biostatistics/1.4.465" target="_blank">Henderson et. al. (2000)</a> to include multi-study, meta-analytic cases. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/joineRmeta/vignettes/joineRmeta.html" target="_blank">vignette</a> for details.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/joineRmeta.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rare" target="_blank">rare</a> v0.1.0: Implements the alternating direction method of multipliers algorithm of <a rel="nofollow" href="arxiv:1803.06675" target="_blank">Yan and Bien (2018)</a> for fitting linear models with tree-based lasso regularization. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/rare/vignettes/rare-vignette.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/rare.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Time Series</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rMEA" target="_blank">rMEA</a> v1.0.0: Provides tools to read, visualize, and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. See <a rel="nofollow" href="doi:10.1037/a0023419" target="_blank">Ramseyer &amp; Tschacher (2011)</a> for an application, and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/rMEA/README.html" target="_blank">README</a> for how to use the package.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/rMEA.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tsfknn" target="_blank">tsfknn</a> v0.1.0: Provides a function to forecast time series using nearest neighbors regression. See <a rel="nofollow" href="doi:10.1007/s10462-017-9593-z" target="_blank">Martinez et al. (2017)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/tsfknn/vignettes/tsfknn.html" target="_blank">vignette</a> for details.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/tsfknn.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=spGARCH" target="_blank">spGARCH</a> v0.1.4: Provides functions to analyze spatial and spatiotemporal autoregressive conditional heteroscedasticity <a rel="nofollow" href="arxiv:1609.00711" target="_blank">Otto, Schmid, Garthoff (2017)</a>, simulation of spatial ARCH-type processes, quasi-maximum-likelihood estimation of the parameters of spARCH models, spatial autoregressive models with spARCH disturbances, diagnostic checks, and visualizations.</p>
<h3>Utilities</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=base2grob" target="_blank">base2grob</a> v0.0.2: Provides a function to convert a base plot function call (using expression or formula) to <code>grob</code> objects that are compatible to the <code>grid</code> ecosystem so that <code>cowplot</code> can be used to align base plots with <code>ggplot</code> objects. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/base2grob/vignettes/base2grob.html" target="_blank">vignette</a> shows how things work.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/base2grob.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=cranly" target="_blank">cranly</a> v0.1: Provides functions to clean, organize, summarize, and visualize CRAN package database information, and also for building package directives networks (depends, imports, suggests, enhances) and collaboration networks. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/cranly/vignettes/cranly.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/cranly.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=osrmr" target="_blank">osrmr</a> v0.1.28: Implements a wrapper around the <a rel="nofollow" href="http://project-osrm.org/" target="_blank">Open Source Routing Machine (OSRM) API</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/osrmr/vignettes/osrmr.html" target="_blank">vignette</a> for details.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/osrmr.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fasterize" target="_blank">fasterize</a> v1.0.0: Provides a fast, drop-in replacement for <code>rasterize()</code> from the <code>raster</code> package that takes <code>sf</code>-type objects and uses the scan line algorithm attributed to [Wylie et al. (1967)](doi:10.<sup>1145</sup>⁄<sub>1465611</sub>.1465619 There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/fasterize/vignettes/using-fasterize.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/fasterize.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=jsr223" target="_blank">jsr223</a> v0.3.1: Provides a high-level integration that makes <code>Java</code> objects easy to use from within <code>R</code>, and an unified interface for integrating <code>R</code> with several programming languages, including <code>Groovy</code>, <code>JavaScript</code>, <code>JRuby</code>, (<code>Ruby</code>), <code>Jython</code> (<code>Python</code>), and <code>Kotlin</code>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/jsr223/vignettes/jsr223.pdf" target="_blank">manual</a> for details.</p>
<h3>Visualization</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=clustree" target="_blank">clustree</a> v0.1.2: Provides functions to produce clustering tree visualizations for interrogating clusterings as resolution increases. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/clustree/vignettes/clustree.html" target="_blank">vignette</a> for details.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/clustree.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=datamaps" target="_blank">datamaps</a> v0.0.2: Enables users to create interactive choropleth maps with bubbles and arcs by coordinates or region name that can be used directly from the console, from <code>RStudio</code>, in <code>Shiny</code> apps, and in <code>R Markdown</code> documents. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/datamaps/vignettes/get_started.html" target="_blank">vignette</a> will help you get started.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/datamaps.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=funnelR" target="_blank">funnelR</a> v0.1.0: Provides functions for creating funnel plots for proportion data, and supports user-defined benchmarks, confidence limits, and estimation methods (e.g., exact or approximate) based on <a rel="nofollow" href="doi:10.1002/sim.1970" target="_blank">Spiegelhalter (2005)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/funnelR/vignettes/introduction.html" target="_blank">Introduction</a> to get started.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/funnelR.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=nVennR" target="_blank">nVennR</a> v0.2.0: Provides an interface for the nVenn algorithm of <a rel="nofollow" href="doi:10.1093/bioinformatics/bty109" target="_blank">Perez-Silva et al. (2018)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/nVennR/vignettes/nVennR.html" target="_blank">vignette</a> for an introduction to the package, and the R package <a rel="nofollow" href="https://cran.r-project.org/package=UpSetR/README.html" target="_blank"><code>UpSetR</code></a> for help interpreting the results.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/nVennR.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=smovie" target="_blank">smovie</a> v1.0.1: Uses the <a rel="nofollow" href="https://cran.r-project.org/package=rpanel" target="_blank">rpanel</a> package to create interactive movies to help students understand statistical concepts. There are movies to: visualize probability distributions (including user-supplied ones); illustrate sampling distributions of the sample mean (central limit theorem); the sample maximum (extremal types theorem); and more. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/smovie/vignettes/smovie-vignette.html" target="_blank">vignette</a> for an overview.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-04-19-Rickert-March-Top40_files/smovie.png?w=456&ssl=1" data-recalc-dims="1"></p>
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