<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/may-2018-top-40-new-packages/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="https://rviews.rstudio.com/2018/06/26/may-2018-top-40-new-packages/"> R Views</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>While looking over the 215 or so new packages that made it to CRAN in May, I was delighted to find several packages devoted to subjects a little bit out of the ordinary; for instance, <code>bioacoustics</code> analyzes audio recordings, <code>freegroup</code> looks at some abstract mathematics, <code>RQEntangle</code> computes quantum entanglement, <code>stemmatology</code> analyzes textual musical traditions, and <code>treedater</code> estimates clock rates for evolutionary models. I take this as evidence that R is expanding beyond its traditional strongholds of statistics and finance as people in other fields with serious analytic and computational requirements become familiar with the language. And, when I see a package from a philologist and scholar of “Ancient and Medieval Worlds”, I am persuaded to think that R is making a unique contribution to computational literacy.</p>
<p>Below are my “Top 40” package picks for May 2018, organized into the following categories: Computational Methods, Data, Data Science, Finance, Mathematics, Music, Science, Statistics, Time Series, Utilities and Visualization.</p>
<h3>Computational Methods</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dqrng" target="_blank">dqrng</a> v0.0.4: Provides fast random number generators with good statistical properties, including the 64-bit variant of the <a rel="nofollow" href="https://dl.acm.org/citation.cfm?doid=272991.272995" target="_blank">Mersenne-Twister</a>, <a rel="nofollow" href="http://www.pcg-random.org/" target="_blank">pcg64</a>, and <a rel="nofollow" href="http://xoshiro.di.unimi.it/" target="_blank">Xoroshiro128 and Xoroshiro256</a>. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/dqrng/vignettes/dqrng.html" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/dqrng/vignettes/parallel.html" target="_blank">Parallel Usage</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=optimParallel" target="_blank">optimParallel</a> v.7-2: Provides a parallel versions of the gradient-based <code>stats::optim()</code> function. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/optimParallel/vignettes/optimParallel.pdf" target="_blank">vignette</a> is informative.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/optimParallel.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Data</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=childesr" target="_blank">childesr</a> v0.1.0: Implements an interface to <a rel="nofollow" href="http://childes-db.stanford.edu/" target="_blank">CHILDES</a>, an open repository for transcripts of parent-child interaction. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/childesr/vignettes/access_childes_db.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=PetfindeR" target="_blank">PetfindeR</a> v1.1.3: Is a wrapper of the <a rel="nofollow" href="https://www.petfinder.com/developers/api-docs" target="_blank">Petfinder API</a> that implements methods for interacting with and extracting data from the <a rel="nofollow" href="https://www.petfinder.com/" target="_blank">Petfinder</a> database, one of the largest online, searchable databases of adoptable animals and animal welfare organizations across North America. See the Getting Started Guide: <a rel="nofollow" href="https://cran.r-project.org/web/packages/PetfindeR/vignettes/PetfindeR_Introduction_Part_One.html" target="_blank">part1</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/PetfindeR/vignettes/PetfindeR_Introduction_Part_Two.html" target="_blank">part2</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/PetfindeR.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Data Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=catch" target="_blank">catch</a> v1.0: Provides functions to perform classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in <a rel="nofollow" href="arxiv:1805.04421" target="_blank">Pan, Mai and Zhang (2018)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SemiSupervised" target="_blank">SemiSupervised</a> v1.0: Implements several safe graph-based semi-supervised learning algorithms. For technical details, refer to <a rel="nofollow" href="http://jmlr.org/papers/v14/culp13a.html" target="_blank">Culp and Ryan (2013)</a>, <a rel="nofollow" href="http://www.jmlr.org/papers/v16/ryan15a.html" target="_blank">Ryan and Culp (2015)</a> and the package <a rel="nofollow" href="https://cran.r-project.org/web/packages/SemiSupervised/vignettes/SemiSupervised.pdf" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=spFSR" target="_blank">spFSR</a> v1.0.0: Offers functions to perform feature selection and ranking via simultaneous perturbation stochastic approximation (SPSA-FSR) based on works by <a rel="nofollow" href="arxiv:1508.07630" target="_blank">Aksakalli and Malekipirbazari (2015)</a> and <a rel="nofollow" href="arxiv:1804.05589" target="_blank">Yenice et al. (2018)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/spFSR/vignettes/spFSR.html" target="_blank">Introduction</a>.</p>
<h3>Finance</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=PortfolioAnalytics" target="_blank">PortfolioAnalytics</a> v1.1.0: Provides functions for portfolio analysis, including numerical methods for portfolio optimization. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/portfolio_vignette.pdf" target="_blank">Introduction</a> and vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/ROI_vignette.pdf" target="_blank">Optimization</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/custom_moments_objectives.pdf" target="_blank">Custom Moment and Objective Functions</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/risk_budget_optimization.pdf" target="_blank">Portfolio Optimization with CVaR Budgets</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sparseIndexTracking" target="_blank">sparseIndexTracking</a> v0.1.0: Provides functions to compute sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation) based on the paper <a rel="nofollow" href="doi:10.1109/TSP.2017.2762286" target="_blank">Benidis et al. (2018)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/sparseIndexTracking/vignettes/SparseIndexTracking-vignette.pdf" target="_blank">vignette</a> shows how to design a portfolio to track an index.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/sparseIndexTracking.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Mathematics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=freegroup" target="_blank">freegroup</a> v1.0: Provides functions to elements of the <a rel="nofollow" href="https://en.wikipedia.org/wiki/Free_group" target="_blank">free group</a>, including inversion, multiplication by a scalar, group-theoretic power operation, and Tietze forms. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/freegroup/vignettes/freevig.html" target="_blank">vignette</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ODEsensitivity" target="_blank">ODEsensitivity</a> v1.1.1: Provides functions to perform sensitivity analysis for ordinary differential equation (ode) models using the <a rel="nofollow" href="https://cran.r-project.org/package=ODEnetwork" target="_blank">ODEnetwork</a> package, which simulates a network of second-order ODEs. See [Weber et al. (2018)(<a rel="nofollow" href="https://eldorado.tu-dortmund.de/handle/2003/36875)" target="_blank">https://eldorado.tu-dortmund.de/handle/2003/36875)</a>] for details, and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/ODEsensitivity/vignettes/ODEsensitivity.html" target="_blank">vignette</a> to get started.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/ODEsensitivity.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Music</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=stemmatology" target="_blank">stemmatoloty</a> v0.3.1: Allows users to explore and analyze the genealogy of textual or musical traditions from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by <a rel="nofollow" href="doi:10.1484/M.LECTIO-EB.5.102565" target="_blank">Camps and Cafiero (2015)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/stemmatology/vignettes/stemmatology.pdf" target="_blank">vignette</a> provides details.</p>
<h3>Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=bioacoustics" target="_blank">bioacoustics</a> v0.1.2: Provides functions to analyze audio recordings and automatically extract animal vocalizations. Contains all the necessary tools to process audio recordings of various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, display audio signals, and detect and extract automatically acoustic features for further analysis such as classification. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/bioacoustics/vignettes/introduction.html" target="_blank">vignette</a> provides an example.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=epiphy" target="_blank">epiphy</a> v0.3.4: Provides a toolbox for analyzing plant disease epidemics and a common framework for plant disease intensity data recorded over time and/or space. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/epiphy/vignettes/defs-and-eqns.html" target="_blank">Definitions and Relationships between Parameters</a> and another with <a rel="nofollow" href="https://cran.r-project.org/web/packages/epiphy/vignettes/epiphy.html" target="_blank">Examples</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/epiphy.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=treedater" target="_blank">treedater</a> v0.2.0: Offers functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models. See <a rel="nofollow" href="doi:10.1093/ve/vex025" target="_blank">Volz and Frost (2017)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/treedater/vignettes/h3n2.html" target="_blank">vignette</a> provides an example using the Influenza H3N2 virus.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RQEntangle" target="_blank">RQEntangle</a> v0.1.0: Provides functions to compute the Schmidt decomposition of bipartite quantum systems, discrete or continuous, and their respective entanglement metrics. See <a rel="nofollow" href="doi:10.1119/1.17904" target="_blank">Ekert and Knight (1995)</a> and the vignettes <a rel="nofollow" href="https://cran.r-project.org/web/packages/RQEntangle/vignettes/CoupledHarmonics.html" target="_blank">Entanglement in Coupled Harmonics</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/RQEntangle/vignettes/CoupledTwoLevelSystems.html" target="_blank">Entanglement between Two Coupled Two-Level Systems</a> for details.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/RQEntangle.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Statistics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=glmmboot" target="_blank">glmmboot</a> v0.1.2: Provides two functions to perform bootstrap resampling for most models that <code>update()</code> works for. <code>BootGlmm()</code> performs block resampling if random effects are present, and case resampling if not; <code>BootCI()</code> converts output from bootstrap model runs into confidence intervals and p-values. See <a rel="nofollow" href="arxiv:1805.08670" target="_blank">Humphrey and Swingley (2018)</a> for the details and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/glmmboot/vignettes/quick_use.html" target="_blank">vignette</a> to get started.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=glmmEP" target="_blank">glmmEP</a> v1.0-1: Allows users to solve Generalized Linear Mixed Model Analysis via Expectation Propagation. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. See the methodology in <a rel="nofollow" href="arxiv:1805.08423v1" target="_blank">Hall et al. (2018)</a>, and the user manual in the <a rel="nofollow" href="https://cran.r-project.org/web/packages/glmmEP/vignettes/manual.pdf" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/glmmEP.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=groupedSurv" target="_blank">groupedSurv</a> v1.0.1: Provides <code>Rcpp</code>-based functions to compute the efficient score statistics for grouped time-to-event data (<a rel="nofollow" href="https://www.jstor.org/stable/pdf/2529588.pdf?seq=1#page_scan_tab_contents" target="_blank">Prentice and Gloeckler (1978)</a>), with the optional inclusion of baseline covariates. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/groupedSurv/vignettes/groupedSurv.pdf" target="_blank">vignette</a> gives an example.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=modeldb" target="_blank">modeldb</a> v0.1.0: Provides functions to fit models inside databases with <code>dplyr</code> backends. There are vignettes showing how to implement <a rel="nofollow" href="https://cran.r-project.org/web/packages/modeldb/vignettes/linear_regression.html" target="_blank">linear regression</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/modeldb/vignettes/kmeans.html" target="_blank">kmeans</a> models.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/modeldb.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=MLZ" target="_blank">MLZ</a> v0.1.1: Provides estimation functions and diagnostic tools for mean length-based total mortality estimators based on <a rel="nofollow" href="doi:10.1577/T05-153.1" target="_blank">Gedamke and Hoenig (2006)</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/MLZ/vignettes/MLZ.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/MLZ.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=NFWdist" target="_blank">NFWdist</a> v0.1.0: Provides density, distribution function, quantile function, and random generation for the 3D Navarro, Frenk &amp; White (NFW) profile. For details see <a rel="nofollow" href="arxiv:1805.09550" target="_blank">Robotham &amp; Howlett (2018)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/NFWdist/vignettes/NFWdist.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/NFWdist.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=survBootOutliers" target="_blank">survBootOutliers</a> v1.0: Offers three new concordance-based methods for outlier detection in a survival context. The methodology is described in two papers by Pinto J., Carvalho A. and Vinga S.: <a rel="nofollow" href="doi:10.5220/0005225300750082" target="_blank">paper1</a> and <a rel="nofollow" href="doi:10.1007/978-3-319-27926-8_22" target="_blank">paper2</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/survBootOutliers/vignettes/survBootOutliers.pdf" target="_blank">vignette</a> provides an introduction.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vinereg" target="_blank">vinereg</a> v0.3.0: Implements D-vine quantile regression models with parametric or non-parametric pair-copulas. See <a rel="nofollow" href="doi:10.1016/j.csda.2016.12.009" target="_blank">Kraus and Czado (2017)</a> and <a rel="nofollow" href="arxiv:1705.08310" target="_blank">Schallhorn et al. (2017)</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/vinereg/vignettes/abalone-example.html" target="_blank">vignette</a> showing how to use the package, and another covering an <a rel="nofollow" href="https://cran.r-project.org/web/packages/vinereg/vignettes/bike-rental.html" target="_blank">Analysis of bike rental data</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/vinereg.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=ASSA" target="_blank">ASSA</a> v1.0: Provides functions to model and decompose time series into principal components using singular spectrum analysis. See <a rel="nofollow" href="doi:10.1016/j.ijforecast.2015.09.004" target="_blank">de Carvalho and Rua (2017)</a> and <a rel="nofollow" href="doi:10.1016/j.econlet.2011.09.007" target="_blank">de Carvalho et al (2012)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=DTWBI" target="_blank">DTWBI</a> v1.0: Provides functions to impute large gaps within time series based on Dynamic Time Warping methods. See <a rel="nofollow" href="doi:10.1016/j.patrec.2017.08.019" target="_blank">Phan et al. (2017)</a>. The <a rel="nofollow" href="http://mawenzi.univ-littoral.fr/DTWBI/" target="_blank">website</a> has examples.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/DTWBI.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=permutes" target="_blank">permutes</a> v0.1: Uses permutation testing (<a rel="nofollow" href="doi:10.1016/j.jneumeth.2007.03.024" target="_blank">Maris &amp; Oostenveld (2007)</a>) to help determine optimal windows for analyzing densely-sampled time series. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/permutes/vignettes/permutes.pdf" target="_blank">vignette</a> provides details.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/permutes.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Utilities</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=bench" target="_blank">bench</a> v1.0.1: Provides tools to benchmark and analyze execution times for R expressions.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/bench.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=conflicted" target="_blank">conflicted</a> v0.1.0: Provides an alternative to R’s default conflict-resolution strategy for R packages.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=diffdf" target="_blank">diffdf</a> v1.0.0: Provides functions for comparing two data frames. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/diffdf/vignettes/diffdf-basic.html" target="_blank">vignette</a> describes how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pkgdown" target="_blank">pkgdown</a> v1.1.0: Provides functions to generate a website from a source package by converting your documentation, vignettes, <code>README</code>, and more to <code>HTML</code>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/pkgdown/pkgdown.pdf" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rtika" target="_blank">rtika</a> v0.1.8: Provides functions to extract text or metadata from over a thousand file types, using <a rel="nofollow" href="https://tika.apache.org/" target="_blank">Apache Tika</a> to produce either plain-text or structured <code>XHTML</code> content. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/rtika/vignettes/rtika_introduction.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tabulizer" target="_blank">tabulizer</a> v0.2.2: Provides bindings for the <a rel="nofollow" href="http://tabula.technology/" target="_blank">Tabula</a> <code>Java</code> library, which can extract tables from PDF documents. The <a rel="nofollow" href="https://github.com/ropensci/tabulizerjars" target="_blank">tabulizerjars</a> packages provides versioned .jar files. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/tabulizer/vignettes/tabulizer.html" target="_blank">Introduction</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=shinytest" target="_blank">shinytest</a> v1.3.0: Enables automated testing of <code>Shiny</code> applications, using a headless browser.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vcr" target="_blank">vcr</a> v0.1.0: A port of the <a rel="nofollow" href="https://github.com/vcr/vcr/" target="_blank">Ruby gem</a> of the same name, <code>vcr</code> enables users to record test suite <code>HTTP</code> requests and replay them during future runs. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/vcr/vignettes/vcr_vignette.html" target="_blank">Introduction</a> and vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/vcr/vignettes/configuration.html" target="_blank">Configuration</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/vcr/vignettes/request_matching.html" target="_blank">Request Matching</a></p>
<h3>Visualization</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=c3" target="_blank">c3</a> v0.2.0: Implements a wrapper (<a rel="nofollow" href="http://www.htmlwidgets.org/" target="_blank">htmlwidget</a>) for the <a rel="nofollow" href="http://c3js.org/" target="_blank"><code>C3.js</code></a> charting library that includes all types of <code>C3.js</code> plots, enabling interactive web-based charts to be embedded in <code>R Markdown</code> documents and <code>Shiny</code> applications. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/c3/vignettes/examples.html" target="_blank">vignette</a> shows basic usage.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/c3.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=chromoMap" target="_blank">chromoMap</a> v0.1: Provides interactive, configurable graphics visualizations of human chromosomes, allowing users to map chromosome elements (like genes, SNPs, etc.) on the chromosome plot, and introduces a special plot, the “chromosome heatmap”, which enables visualizing data associated with chromosome elements. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/chromoMap.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ExPanDaR" target="_blank">ExPanDaR</a> v0.2.0: Provides a shiny-based front end and a set of functions for exploratory panel data analysis. Run as a web-based app, it enables users to assess the robustness of empirical evidence without providing them access to the underlying data. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/ExPanDaR/vignettes/ExPanDaR-functions.html" target="_blank">using the package</a> and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/ExPanDaR/vignettes/use_ExPanD.html" target="_blank">panel data exploration</a>.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/ExPanDaR.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ggdistribute" target="_blank">ggdistribute</a> v1.0.1: Extends <code>ggplot2</code> for plotting posterior or other types of unimodal distributions that require overlaying information about a distribution’s intervals. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/ggdistribute/vignettes/geom_posterior.html" target="_blank">vignette</a> provides examples.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/ggdistribute.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=r2d3" target="_blank">r2d3</a> v0.2.2: Provides a suite of tools for using the <a rel="nofollow" href="https://d3js.org/" target="_blank"><code>D3</code></a> library to produce dynamic, interactive data visualizations. There are vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/r2d3/vignettes/advanced_rendering.html" target="_blank">Advanced Rendering with Callbacks</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/r2d3/vignettes/data_conversion.html" target="_blank">R to D3 Data Conversion</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/r2d3/vignettes/dependencies.html" target="_blank">CSS &amp; JavaScript Dependencies</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/r2d3/vignettes/learning_d3.html" target="_blank">Learning D3</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/r2d3/vignettes/package_development.html" target="_blank">Package Development</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/r2d3/vignettes/visualization_options.html" target="_blank">D3 Visualization Options</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-06-18-Rickert-May-Top40_files/r2d3.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p> </p>
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