<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/november-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/2018/12/21/november-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>Having absorbed an average of 181 new packages each month over the last 28 months, CRAN is still growing at a pretty amazing rate. The following plot shows the number of new packages since I started keeping track in August 2016.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/new_pkgs.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p>This November, 171 new packages stuck to CRAN. Here is my selection for the “Top 40” organized into the categories: Computational Methods, Data, Finance, Machine Learning, Marketing Analytics, Science, Statistics, Utilities and Visualization.</p>
<h3>Computational Methods</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=mixsqp" target="_blank">mixsqp</a> v0.1-79: Provides optimization algorithms (<a rel="nofollow" href="arxiv:1806.01412" target="_blank">Kim et al. (2012)</a> based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/mixsqp/vignettes/mixsqp-intro.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=polylabelr" target="_blank">polylabelr</a> v0.1.0: Implements a wrapper around the C++ library <a rel="nofollow" href="https://github.com/mapbox/polylabel" target="_blank">polylabel</a> from <code>Mapbox</code>, providing an efficient routine for finding the approximate pole of inaccessibility of a polygon. See <a rel="nofollow" href="https://cran.r-project.org/web/packages/polylabelr/readme/README.html" target="_blank">README</a>.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/polylabel.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RiemBase" target="_blank">Riembase</a> v0.2.1: Implements a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. See <a rel="nofollow" href="doi:10.1017/CBO9781139094764" target="_blank">Bhattacharya and Bhattacharya (2012)</a> if you are interested in statistics on manifolds, and <a rel="nofollow" href="https://www.abebooks.com/servlet/BookDetailsPL?bi=30175283776&searchurl=isbn%3D978-0-691-13298-3%26sortby%3D17&cm_sp=snippet-_-srp1-_-title1" target="_blank">Absil et al (2007)</a> for information on the computational aspects of optimization on matrix manifolds.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SolveRationalMatrixEquation" target="_blank">SolveRationalMatrixEquation</a> v0.1.0: Provides functions to find the symmetric positive definite solution X such that X = Q + L (X inv) L^T given a symmetric positive definite matrix Q and a non-singular matrix L. See <a rel="nofollow" href="doi:10.1155/2007/21850" target="_blank">Benner et al. (2007)</a> for the details and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/SolveRationalMatrixEquation/vignettes/SolveRationalMatrixEquation.html" target="_blank">vignette</a> for an example.</p>
<h3>Data</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=metsyn" target="_blank">metsyn</a> v0.1.2: Provides an interface to the <a rel="nofollow" href="https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32" target="_blank">Meteo France Synop data</a> <a rel="nofollow" href="https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32" target="_blank">API</a>. This is meteorological data recorded every 3 on 62 French meteorological stations.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/metsyn.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=neonUtilities" target="_blank">neonUtilities</a> v1.0.1: Provides an interface to the <a rel="nofollow" href="http://data.neonscience.org/" target="_blank">National Ecological Observatory</a> <a rel="nofollow" href="http://data.neonscience.org/data-api" target="_blank">NEON API</a>. For more information, see the <a rel="nofollow" href="https://github.com/NEONScience/NEON-utilities" target="_blank">README file</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=phenocamapi" target="_blank">phenocamapi</a> v0.1.2: Allows users to obtain phenological time-series and site metadata from the <a rel="nofollow" href="https://phenocam.sr.unh.edu/webcam/" target="_blank">PhenoCam network</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/phenocamapi/vignettes/getting_started_phenocam_api.html" target="_blank">Getting Started Guide</a> and a <a rel="nofollow" href="https://cran.r-project.org/web/packages/phenocamapi/vignettes/phenocam_data_fusion.html" target="_blank">vignette</a> with examples.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/phenocamapi.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rdbnomics" target="_blank">rdbnomics</a> v0.4.3: Provides access to hundreds of millions data series from <a rel="nofollow" href="https://db.nomics.world/" target="_blank">DBnomics API</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/rdbnomics/vignettes/rdbnomics-tutorial.html" target="_blank">vignette</a> for examples.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/rdbnomics.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=restez" target="_blank">restez</a> v1.0.0: Allows users to download large sections of <a rel="nofollow" href="https://www.ncbi.nlm.nih.gov/genbank/" target="_blank">GenBank</a> and generate a local SQL-based database. A user can then query this database using <code>restez</code> functions.</p>
<h3>Finance</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=" target="_blank">crseEventStudy</a> v1.0: Implements the <a rel="nofollow" href="doi:10.1016/j.jempfin.2018.02.004" target="_blank">Dutta et al. (2018)</a> standardized test for abnormal returns in long-horizon event studies to improve the power and robustness of the tests described in <a rel="nofollow" href="doi:10.1016/B978-0-444-53265-7.50015-9" target="_blank">Kothari/Warner (2007)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=psymonitor" target="_blank">psymonitor</a> v0.0.1: Provides functions to apply the real-time monitoring strategy proposed by <a rel="nofollow" href="doi:10.1111/iere.12132" target="_blank">Phillips, Shi and Yu (2015)</a> (and <a rel="nofollow" href="doi:10.1111/iere.12131" target="_blank">here</a>) to test for “bubbles”. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/psymonitor/vignettes/illustrationBONDS.html" target="_blank">detecting crises</a> and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/psymonitor/vignettes/illustrationSNP.html" target="_blank">monitoring bubbles</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/psymonitor.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<h3>Machine Learning</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pivmet" target="_blank">pivmet</a> v0.1.0: Provides a collection of pivotal algorithms for relabeling the MCMC chains in order to cope with the label switching problem in Bayesian mixture models. Functions also initialize the centers of the classical k-means algorithm in order to obtain a better clustering solution. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/pivmet/vignettes/K-means_clustering_using_the_MUS_algorithm.html" target="_blank">K-means clustering</a> and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/pivmet/vignettes/Relabelling_in_Bayesian_mixtures_by_pivotal_units.html" target="_blank">Label Swithching</a>.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/pivmet.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RDFTensor" target="_blank">RDFTensor</a> v1.0: Implements tensor factorization techniques suitable for sparse, binary and three-mode <code>RDF</code> tensors. See <a rel="nofollow" href="doi:10.1145/2187836.2187874" target="_blank">Nickel et al. (2012)</a>, <a rel="nofollow" href="doi:10.1038/44565" target="_blank">Lee and Seung</a>, <a rel="nofollow" href="doi:10.1007/978-3-642-33460-3_39" target="_blank">Papalexakis et al.</a> and <a rel="nofollow" href="doi:10.1137/110859063" target="_blank">Chi and T. G. Kolda (2012)</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rfviz" target="_blank">rfviz</a> v1.0.0: Provides an interactive data visualization and exploration toolkit that implements Breiman and Cutler’s original Java based, random forest visualization tools. It includes both supervised and unsupervised classification and regression algorithms. The <a rel="nofollow" href="https://www.stat.berkeley.edu/~breiman/RandomForests/cc_graphics.htm" target="_blank">Berkekey website</a> describes the original implementation.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rJST" target="_blank">rJST</a> v1.0: Provides functions to stimulate the Joint Sentiment Topic model as described by <a rel="nofollow" href="doi:10.1145/1645953.1646003" target="_blank">Lin and He (2009)</a> and <a rel="nofollow" href="doi:10.1109/TKDE.2011.48" target="_blank">Lin et al. (2012)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/rJST/vignettes/rJST.html" target="_blank">Introduction</a> for details.</p>
<h3>Marketing Analytics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=MarketMatching" target="_blank">Marketmatching</a> v1.1.1: Enables users to find the best control markets using time series matching and analyze the impact of an intervention. Uses the <code>dtw</code> package to do the matching and the <code>CausalImpact</code> package to analyze the causal impact. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/MarketMatching/vignettes/MarketMatching-Vignette.html" target="_blank">vignette</a> for an example.</p>
<h3>Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=EpiSignalDetection" target="_blank">EpiSignalDetection</a> v0.1.1: Provides functions to detect possible outbreaks using infectious disease surveillance data at the European Union / European Economic Area or country level. See <a rel="nofollow" href="doi:10.18637/jss.v070.i10" target="_blank">Salmon et al. (2016)</a> for a description of the automatic detection tools and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/EpiSignalDetection/vignettes/EpiSignalDetection_Vignette.html" target="_blank">vignette</a> for an overview of the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=memnet" target="_blank">memnet</a> v0.1.0: Implements network science tools to facilitate research into human (semantic) memory including several methods to infer networks from verbal fluency data, various network growth models, diverse random walk processes, and tools to analyze and visualize networks. See <a rel="nofollow" href="doi:10.31234/osf.io/s73dp" target="_blank">Wulff et al. (2018)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/memnet/vignettes/memnet.html" target="_blank">vignette</a> for an introduction.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/memnet.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=phylocomr" target="_blank">phylocomr</a> v0.1.2: Implements an interface to <a rel="nofollow" href="http://phylodiversity.net/phylocom/" target="_blank">Phylocom</a>, a library for analysis of <code>phylogenetic</code> community structure and character evolution. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/phylocomr/vignettes/phylocomr_vignette.html" target="_blank">vignette</a> for and introduction to the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=plinkQC" target="_blank">plinkQC</a> v0.2.0: Facilitates genotype quality control for genetic association studies as described by <a rel="nofollow" href="doi:10.1038/nprot.2010.116" target="_blank">Anderson et al. (2010)</a>. There are vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/plinkQC/vignettes/AncestryCheck.pdf" target="_blank">Ancestry Estimation</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/plinkQC/vignettes/Genomes1000.pdf" target="_blank">Processing 1000 Genomes</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/plinkQC/vignettes/HapMap.pdf" target="_blank">HapMap III Data</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/plinkQC/vignettes/plinkQC.pdf" target="_blank">Genotype Quality Control</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/plinkQC.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<h3>Statistics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=BivRec" target="_blank">BivRec</a> v1.0.0: Implements a collection of non-parametric and semiparametric methods to analyze alternating recurrent event data. See <a rel="nofollow" href="https://cran.r-project.org/web/packages/BivRec/readme/README.html" target="_blank">README</a> for examples.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/BivRec.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=cusum" target="_blank">cusum</a> v0.1.0: Provides functions for constructing and evaluating <a rel="nofollow" href="https://en.wikipedia.org/wiki/CUSUM" target="_blank">CUSUM charts</a> and RA-CUSUM charts with focus on false signal probability. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/cusum/vignettes/cusum.html" target="_blank">vignette</a> offers an example.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dabestr" target="_blank">dabestr</a> v0.1.0: Offers an alternative to significance testing using bootstrap methods and estimation plots. See <a rel="nofollow" href="doi:10.1101/377978" target="_blank">Ho et al (2018)</a>. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/dabestr/vignettes/bootstrap-confidence-intervals.html" target="_blank">Bootstrap Confidence Intervals</a>, another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/dabestr/vignettes/robust-statistical-visualization.html" target="_blank">Statistical Visualizations</a>, and a third on creating <a rel="nofollow" href="https://cran.r-project.org/web/packages/dabestr/vignettes/using-dabestr.html" target="_blank">Estimation Plots</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/dbestr.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=deckgl" target="_blank">deckgl</a> v0.1.8: Implements an interface to <a rel="nofollow" href="https://deck.gl/" target="_blank">deck.gl</a>, a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large data sets and supports basemaps from <a rel="nofollow" href="https://www.mapbox.com/" target="_blank">mapbox</a>. There are fourteen brief vignettes, each devoted to a different plot layer, but look <a rel="nofollow" href="https://crazycapivara.github.io/deckgl/" target="_blank">here</a> for a brief overview.</p>
<p><img width="450" src="https://rviews.rstudio.com/2018/12/21/november-2018-top-40-new-packages/" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=LindleyPowerSeries" target="_blank">LindleyPowerSeries</a> v0.1.0: Provides functions to compute the probability density function, the cumulative distribution function, the hazard rate function, the quantile function and random generation for Lindley Power Series distributions. See <a rel="nofollow" href="doi:10.1007/s13171-018-0150-x" target="_blank">Nadarajah and Si (2018)</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=modi" target="_blank">modi</a> v0.1.0: Implements algorithms that take sample designs into account to detect multivariate outliers. See <a rel="nofollow" href="doi:10.17713/ajs.v45i1.86" target="_blank">Bill and Hulliger (2016)</a> for details and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/modi/vignettes/modi_vignette.html" target="_blank">vignette</a> for an introduction.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=MPTmultiverse" target="_blank">MPTmultiverse</a> v0.1: Provides a function to examine the multiverse of possible modeling choices. See the paper by <a rel="nofollow" href="doi:10.1177/1745691616658637" target="_blank">Steegen et al. (2016)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/MPTmultiverse/vignettes/introduction-bayen_kuhlmann_2011.html" target="_blank">vignette</a> for an overview of the package.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/MPTmultiverse.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pterrace" target="_blank">pterrace</a> v1.0: Provides functions to plot the persistence terrace, a summary graphic for topological data analysis that helps to determine the number of significant topological features. See <a rel="nofollow" href="doi:10.1080/10618600.2017.1422432" target="_blank">Moon et al. (2018)</a>.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/pterrace.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=randcorr" target="_blank">randcorr</a> v1.0: Implements the algorithm by <a rel="nofollow" href="doi:10.1016/j.spl.2015.06.015" target="_blank">Pourahmadi and Wang (2015)</a> for generating a random p x p correlation matrix by representing the correlation matrix using Cholesky factorization and hyperspherical coordinates. See <a rel="nofollow" href="arxiv:1809.05212" target="_blank">Makalic and Schmidt (2018)</a> for the sampling process used.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SMFilter" target="_blank">SMFilter</a> v1.0.3: Provides filtering algorithms for the state space models on the <a rel="nofollow" href="https://en.wikipedia.org/wiki/Stiefel_manifold" target="_blank">Stiefel manifold</a> as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and <a rel="nofollow" href="https://www.sciencedirect.com/science/article/pii/S0047259X02000659" target="_blank">matrix Langevin-Bingham distributions</a> on the Stiefel manifold. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/SMFilter/vignettes/readme.html" target="_blank">vignette</a>.</p>
<h3>Utilities</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=IRkernel" target="_blank">IRkernel</a> v0.8.14: Implements a native R kernel for <a rel="nofollow" href="https://jupyter.org/" target="_blank">Jupyter Notebook</a>. See <a rel="nofollow" href="https://cran.r-project.org/web/packages/IRkernel/readme/README.html" target="_blank">README</a> for information on how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=lobstr" target="_blank">lobstr</a> v1.0.0: Provides set of tools for inspecting and understanding R data structures inspired by <code>str()</code>. See <a rel="nofollow" href="https://cran.r-project.org/web/packages/lobstr/readme/README.html" target="_blank">README</a> for information on the included functions.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=parsnip" target="_blank">parsnip</a> v0.0.1: Implements a common interface allowing users to specify a model without having to remember the different argument names across different functions or computational engines. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/parsnip/vignettes/parsnip_Intro.html" target="_blank">vignette</a> goes over the basics.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pkgsearch" target="_blank">pkgsearch</a> v2.0.1: Allows users to search CRAN R packages using the <a rel="nofollow" href="https://www.r-pkg.org/" target="_blank">METACRAN</a> search server.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=stevedore" target="_blank">stevedore</a> v0.9.0: Implements an interface to the <a rel="nofollow" href="https://docs.docker.com/develop/sdk/" target="_blank">Docker API</a>. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/stevedore/vignettes/stevedore.html" target="_blank">Introduction</a> and a vignette with <a rel="nofollow" href="https://cran.r-project.org/web/packages/stevedore/vignettes/examples.html" target="_blank">Examples</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vctrs" target="_blank">vctrs</a> v0.1.0: Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling. There are vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/vctrs/vignettes/s3-vector.html" target="_blank">S3 vectors</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/vctrs/vignettes/stability.html" target="_blank">Type and Size Stability</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/vctrs/vignettes/type-size.html" target="_blank">Prototypes</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vtree" target="_blank">vtree</a> v0.1.4: Provides a function for drawing drawing <code>variable trees</code> plots that display information about hierarchical subsets of a data frame defined by values of categorical variables. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/vtree/vignettes/vtree.html" target="_blank">vignette</a> offers an introduction.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/vtree.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=zipR" target="_blank">zipR</a> v0.1.0: Implements the Python <code>zip()</code> function in R. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/zipR/vignettes/my-vignette.html" target="_blank">vignette</a>.</p>
<h3>Visualization</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=countcolors" target="_blank">countcolors</a> v0.9.0: Contains functions to count colors within color range(s) in images, and provides a masked version of the image with targeted pixels changed to a different color. Output includes the locations of the pixels in the images, and the proportion of the image within the target color range with optional background masking. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/countcolors/vignettes/Introduction.html" target="_blank">Introduction</a> and an <a rel="nofollow" href="https://cran.r-project.org/web/packages/countcolors/vignettes/bat_WNS.html" target="_blank">Example</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/countcolors.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=coveffectsplot" target="_blank">coveffectsplot</a> v0.0.1: Provides forest plots to visualize covariate effects from either the command line or an interactive <code>Shiny</code> application. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/coveffectsplot/vignettes/introduction_to_coveffectsplot.html" target="_blank">Introduction</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-12-14-NovTop40_files/coveffectsplot.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p> </p>
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