<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/july-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/2018/08/27/july-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>July was a big month for submitting new packages to CRAN; by my count, 251 unique and truly new packages were accepted. In addition to quantity, I was pleased to see quality and variety. For instance, <code>tropicalSparse</code>, a package for exploring some abstract mathematics, and <code>eChem</code>, a package for teaching analytical chemistry, exemplify R’s expansion into new fields.</p>
<p>Below are my “Top 40” picks organized into ten categories: Computational Methods, Data, Econometrics, Machine Learning, Mathematics, Science, Statistics, Time Series, Utilities, and Visualization</p>
<h3>Computational Methods</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=osqp" target="_blank">osqp</a> v0.4.0: Provides bindings to the <code>OSQP</code> solver, a numerical optimization package for solving convex quadratic programs written in <code>C</code> based on the alternating direction method of multipliers. See <a rel="nofollow" href="https://arxiv.org/abs/1711.08013" target="_blank">Stellato et al. (2018)</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sundialr" target="_blank">sundailr</a> v0.1.1: Provides a way to call the functions in <a rel="nofollow" href="https://computation.llnl.gov/projects/sundials" target="_blank"><code>SUNDIALS</code></a> C ODE solving library. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/sundialr/vignettes/my-vignette.html" target="_blank">vignette</a>.</p>
<h3>Data</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fredr" target="_blank">fredr</a> v1.0.0: Provides an R client for the <a rel="nofollow" href="https://api.stlouisfed.org/" target="_blank">Federal Reserve Economic Data (FRED)</a>. There are vignettes on FRED <a rel="nofollow" href="https://cran.r-project.org/web/packages/fredr/vignettes/fredr-categories.html" target="_blank">Categories</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/fredr/vignettes/fredr-releases.html" target="_blank">Releases</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/fredr/vignettes/fredr-series.html" target="_blank">Series</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/fredr/vignettes/fredr-sources.html" target="_blank">Sources</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/fredr/vignettes/fredr-tags.html" target="_blank">Tags</a>, as well as a <a rel="nofollow" href="https://cran.r-project.org/web/packages/fredr/vignettes/fredr.html" target="_blank">Getting Started Guide</a>.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/FRED.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=jstor" target="_blank">jstor</a> v0.3.2: Provides functions to import metadata, ngrams, and full-texts delivered by Data for Research by JSTOR. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/jstor/vignettes/introduction.html" target="_blank">Introduction</a>, and vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/jstor/vignettes/automating-file-import.html" target="_blank">Automating File Import</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/jstor/vignettes/known-quirks.html" target="_blank">Known Quirks</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rLandsat" target="_blank">rLandsat</a> v0.1.0: Provides functions to search and acquire <a rel="nofollow" href="https://landsat.usgs.gov/" target="_blank">Landsat</a> data using an API built by <a rel="nofollow" href="https://api.developmentseed.org/satellites" target="_blank">Development Seed</a> and the <a rel="nofollow" href="https://espa.cr.usgs.gov/api" target="_blank">U.S. Geological Survey</a>. See <a rel="nofollow" href="https://cran.r-project.org/web/packages/rLandsat/readme/README.html" target="_blank">README</a> for how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=weathercan" target="_blank">weathercan</a> v0.2.7: Provides tools for downloading historical weather data from the Environment and <a rel="nofollow" href="http://climate.weather.gc.ca/historical_data/search_historic_data_e.html" target="_blank">Climate Change Canada</a> website. Data can be downloaded from multiple stations over large date ranges, and automatically processed into a single dataset. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/weathercan/vignettes/weathercan.html" target="_blank">Introduction</a>, a <a rel="nofollow" href="https://cran.r-project.org/web/packages/weathercan/vignettes/glossary.html" target="_blank">Glossary</a>, and vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/weathercan/vignettes/flags.html" target="_blank">Flags</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/weathercan/vignettes/interpolate_data.html" target="_blank">Interpolation</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/weathercan.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Econometrics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=beezdemand" target="_blank">beezdemand</a> v0.1.0: Provides tools to facilitate analyses performed in studies of behavioral economic demand such as data screening proposed by <a rel="nofollow" href="https://www.ncbi.nlm.nih.gov/pubmed/26147181" target="_blank">Stein et al.(2015)</a>, and model fitting, including linear <a rel="nofollow" href="https://link.springer.com/chapter/10.1007/978-94-009-2470-3_22" target="_blank">Hursh et al. (1989)</a>, exponential <a rel="nofollow" href="https://www.ncbi.nlm.nih.gov/pubmed/18211190" target="_blank">Hursh &amp; Silberberg (2008)</a>, and modified exponential <a rel="nofollow" href="https://www.researchgate.net/publication/281143353_A_Modified_Exponential_Behavioral_Economic_Demand_Model_to_Better_Describe_Consumption_Data" target="_blank">Koffarnus et al. (2015)</a> models. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/beezdemand/vignettes/beezdemand.html" target="_blank">vignette</a> provides examples.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/beezdemand.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sgmodel" target="_blank">sgmodel</a> v0.1.0: Provides functions to compute the solutions of a generic stochastic growth model for a given set of user-supplied parameters. See <a rel="nofollow" href="https://www.sciencedirect.com/science/article/pii/002205317190038X" target="_blank">Merton (1971)</a> and <a rel="nofollow" href="https://www.bibsonomy.org/bibtex/0619634620978e8524622d3c0f60185c?postOwner=smicha&intraHash=490b9c3154a96743c291b6d185f7337f" target="_blank">Tauchen (1986)</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/sgmodel/vignettes/sgmodel_vignette.html" target="_blank">vignette</a>.</p>
<h3>Machine Learning</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=bigdatadist" target="_blank">bigdatadist</a> v1.0: Provides functions to compute distances between probability measures, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details see <a rel="nofollow" href="doi:10.3233/IDA-140706" target="_blank">Martos et al (2014)</a> and <a rel="nofollow" href="doi:10.3390/e20010033" target="_blank">Martos et al (2018)</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/bigdatadist.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=L0Learn" target="_blank">L0Learn</a> v1.0.2: Provides an optimized toolkit for approximately solving L0-regularized learning problems. The algorithms are based on coordinate descent and local combinatorial search. For more details see <a rel="nofollow" href="https://arxiv.org/abs/1803.01454" target="_blank">Hazimeh and Mazumder (2018)</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/L0Learn/vignettes/L0Learn-vignette.html" target="_blank">vignette</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/L0L.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<h3>Mathematics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tropicalSparse" target="_blank">tropicalSparse</a> v0.1.0: Implements some basic tropical algebra functionality for sparse matrices by applying sparse matrix storage techniques. These include addition and multiplication of vectors and matrices, dot product of the vectors in tropical form, and some general equations are also solved using tropical algebra. Look <a rel="nofollow" href="https://math.berkeley.edu/~bernd/mathmag.pdf" target="_blank">here</a> for the math.</p>
<h3>Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=eChem" target="_blank">eChem</a> v1.0.0: Provides tools for use in courses in analytical chemistry. Functions simulate cyclic voltammetry, linear-sweep voltammetry, single-pulse and double-pulse chronoamperometry, and chronocoulometry experiments using the implicit finite difference method outlined in <a rel="nofollow" href="https://pubs.acs.org/doi/10.1021/acs.jchemed.5b00225" target="_blank">Brown (2015)</a>. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/eChem/vignettes/Overview.pdf" target="_blank">Overview</a> and vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/eChem/vignettes/Using_eChem.pdf" target="_blank">Using eChem</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/eChem/vignettes/Computational_Details.pdf" target="_blank">Computational details</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/eChem/vignettes/Additional_Examples.pdf" target="_blank">Examples</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/echem.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RaceID" target="_blank">RaceID</a> v0.1.1: Enables inference of cell types and prediction of lineage trees using the StemID2 algorithm of <a rel="nofollow" href="https://www.nature.com/articles/nmeth.4662" target="_blank">Herman, Sagar and GrÃ¼n D. (2018)</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/RaceID/vignettes/RaceID.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/RaceID.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=updog" target="_blank">updog</a> v1.0.1: Implements empirical Bayes approaches to genotype polyploids from next-generation sequencing data while accounting for allelic bias, over dispersion, and sequencing error. See <a rel="nofollow" href="https://www.biorxiv.org/content/early/2018/08/02/281550" target="_blank">Gerard et al. (2018)</a> for implementation details, along with vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/updog/vignettes/oracle_calculations.html" target="_blank">Oracle Calculations</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/updog/vignettes/parallel_computing.html" target="_blank">Parallization</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/updog/vignettes/simulate_ngs.html" target="_blank">Simulating Sequeencing Data</a>, and an <a rel="nofollow" href="https://cran.r-project.org/web/packages/updog/vignettes/smells_like_updog.html" target="_blank">Example</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/updog.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<h3>Statistics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=adaptMT" target="_blank">adaptMT</a> v1.0.0: Implements adaptive p-value thresholding (AdaPT), including a framework that allows users to specify any algorithm to learn local false-discovery rate, as well as a pool of convenient functions that implement specific algorithms. See <a rel="nofollow" href="arxiv:1609.06035" target="_blank">Lei and Fithian (2016)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/adaptMT/vignettes/adapt_demo.html" target="_blank">vignette</a> provides an introduction to the package.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/adaptMT.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=biglmm" target="_blank">biglmm</a> v0.9-1: Provides regression for data too large to fit in memory. This package functions exactly like the <a rel="nofollow" href="https://cran.r-project.org/package=biglm" target="_blank">biglm</a> package, but works with later versions of R.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=circumplex" target="_blank">circumplex</a> v0.1.2: Provides tools for analyzing and visualizing circular data, including a generalization of the bootstrapped structural summary method from <a rel="nofollow" href="doi:10.1177/1073191115621795" target="_blank">Zimmermann &amp; Wright (2017)</a>, and functions for creating publication-ready tables and figures from the results. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/circumplex/vignettes/introduction-to-ssm-analysis.html" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/circumplex/vignettes/introduction-to-ssm-analysis.html" target="_blank">Analysis</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/circumplex.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=MultiFit" target="_blank">MultiFit</a> v0.1.2: Provides functions to test for independence of two random vectors and learn and report the dependency structure. For more information, see <a rel="nofollow" href="arxiv:1806.06777" target="_blank">Gorsky and Ma (2018)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/MultiFit/vignettes/multiFit.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/MultiFit.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=PHEindicatormethods" target="_blank">PHEIndicatormethods</a> v1.0.8: Provides functions to calculate commonly used public health statistics and their confidence intervals using methods approved for use in the production of Public Health England indicators, such as those presented via <a rel="nofollow" href="http://fingertips.phe.org.uk/" target="_blank">Fingertips</a>. The statistical methods are referenced in the following publications: <a rel="nofollow" href="https://rviews.rstudio.com/2018/08/27/july-2018-top-40-new-packages/1987" target="_blank">Breslow and Day</a>](doi:10.1002/sim.4780080614), <a rel="nofollow" href="doi:10.1002/sim.4780100317" target="_blank">Dobson et al (1991)</a>, <a rel="nofollow" href="doi:10.1002/9780470773666" target="_blank">Armitage and Berry (2002)</a>, and <a rel="nofollow" href="doi:10.1080/01621459.1927.1050295" target="_blank">Wilson (1927)</a>. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/PHEindicatormethods/vignettes/DSR-vignette.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=robmixglm" target="_blank">robmixglm</a> v1.0-2: Implements robust generalized linear models (GLM) using a mixture method, as described in <a rel="nofollow" href="doi:10.1080/02664763.2017.1414164" target="_blank">Beath (2018)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/robmixglm/vignettes/robmixglm-package.pdf" target="_blank">vignette</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SingleCaseES" target="_blank">SingelCaseES</a> v0.4.0: Provides functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by <a rel="nofollow" href="doi:10.1080/00273171.2018.1466681" target="_blank">Swan and Pustejovsky (2018)</a>. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/SingleCaseES/vignettes/Effect-size-definitions.html" target="_blank">Definitions and Mathematical Details</a> and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/SingleCaseES/vignettes/Using-SingleCaseES.html" target="_blank">Calculations</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=spCP" target="_blank">spCP</a> v1.0: Implements a spatially varying change-point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/spCP/vignettes/spCP-example.html" target="_blank">vignette</a> for an example.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/spCp.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=TDAstats" target="_blank">TDAstats</a> v0.3.0: Provides a tool set for topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. For a general background on computing persistent homology for topological data analysis, see <a rel="nofollow" href="doi:10.1140/epjds/s13688-017-0109-5" target="_blank">Otter et al. (2017)</a>. To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read <a rel="nofollow" href="doi:10.1007/s41468-017-0008-7" target="_blank">Robinson &amp; Turner (2017)</a>. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/TDAstats/vignettes/intro.html" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/TDAstats/vignettes/inference.html" target="_blank">Hypothesis Testing with TDA</a>.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/TDAstats.png?w=450&ssl=1" data-recalc-dims="1" height="300"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=trafo" target="_blank">trafo</a> v1.0.0: Provides functions to estimate, select, and compare several families of transformations, including Bickel-Doksum <a rel="nofollow" href="doi:10.2307/2287831" target="_blank">Bickel and Doksum (1981)</a>, Box-Cox, Dual <a rel="nofollow" href="doi:10.1016/j.econlet.2006.01.011" target="_blank">Yang (2006)</a>, Glog <a rel="nofollow" href="doi:10.1093/bioinformatics/18.suppl_1.S105" target="_blank">Durbin et al. (2002)</a>, Gpower1, Log, Log-shift opt <a rel="nofollow" href="doi:10.1002/sta4.104" target="_blank">Feng et al. (2016)</a>, Manly, Modulus <a rel="nofollow" href="doi:10.2307/2986305" target="_blank">John and Draper (1980)</a>, Neglog <a rel="nofollow" href="doi:10.1111/j.1467-9876.2005.00520.x" target="_blank">Whittaker et al. (2005)</a>, Reciprocal and Yeo-Johnson. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/trafo/vignettes/vignette_trafo.pdf" target="_blank">vignette</a> for the math.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=uniformly" target="_blank">uniformly</a> v0.1.0: Provides functions to uniformly sample from various geometric shapes, such as spheres, ellipsoids, simplices. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/uniformly/vignettes/convexhull.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/uniformly.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=rollRegres" target="_blank">rollRegress</a> v0.1.0: Implements methods for fast-rolling and expanding linear regression models. The methods use rank-one updates and downdates of the upper triangular matrix from a QR decomposition. See <a rel="nofollow" href="doi:10.1137/1.9781611971811" target="_blank">Dongarra et al.(1979)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/rollRegres/vignettes/Comparisons.html" target="_blank">vignette</a> provides some details.</p>
<h3>Utilities</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=anyLib" target="_blank">anyLib</a> v1.0.4: Provides functions to install and load a list of packages from CRAN, Bioconductor or GitHub. For GitHub, if you do not have the full path with the maintainer name in it (e.g. “achateigner/topReviGO”), it will be able to load it but not to install the package. There is a brief <a rel="nofollow" href="https://cran.r-project.org/web/packages/anyLib/vignettes/help.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dbx" target="_blank">dbx</a> v0.2.1: Provides select, insert, update, upsert, and delete database operations for <code>PostgreSQL</code>, <code>MySQL</code>, <code>SQLite</code>, and other databases. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/dbx/readme/README.html" target="_blank">README</a> for usage</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=envnames" target="_blank">envnames</a> v0.3.0: Provides functions to keep track of user-defined environment names that cannot be retrieved with the base R function <code>environmentName()</code>. The main function in this package, <code>environment_name()</code>, returns the name of the environment given as parameter. The vignette offers an <a rel="nofollow" href="https://cran.r-project.org/web/packages/envnames/vignettes/envnames.pdf" target="_blank">overview</a> of the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=librarian" target="_blank">librarian</a> v1.3.0: Provides functions to automatically install, update, and load CRAN and GitHub packages in a single function call. See <a rel="nofollow" href="https://cran.r-project.org/web/packages/librarian/readme/README.html" target="_blank">README</a> for usage.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=makeParallel" target="_blank">makeParallel</a> v0.1.1: Provides functions to automate the transformation of serial R code into more efficient parallel versions. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/makeParallel/vignettes/quickstart.html" target="_blank">Quickstart Guide</a> and a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/makeParallel/vignettes/concepts.html" target="_blank">Parallel Concepts</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/makeParallel.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=metaDigitise" target="_blank">metaDigitise</a> v1.0.0: Provides functions to extract, summarize and digitize data from published figures in research papers. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/metaDigitise/vignettes/metaDigitise.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RSuite" target="_blank">RSuite</a> v0.32-244: Provides a set of tools to be used with the <a rel="nofollow" href="http://rsuite.io/" target="_blank">R Suite</a> for developing data-science workflows.</p>
<h3>Visualization</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ceterisParibus" target="_blank">ceterisParibus</a> v0.3.0: Provides functions to create “What if?” plots of model responses around selected points in a feature space. The four vignettes offer several examples, including a <a rel="nofollow" href="https://cran.r-project.org/web/packages/ceterisParibus/vignettes/ceteris_paribus.html" target="_blank">Random Forests Example</a> and a <a rel="nofollow" href="https://cran.r-project.org/web/packages/ceterisParibus/vignettes/ceteris_paribus_HR.html" target="_blank">Classification Example</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/ceterisParibus.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=cytofan" target="_blank">cytofan</a> v0.1.0: Implements fan plots for cytometry data in <code>ggplot2</code>. See <a rel="nofollow" href="https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart" target="_blank">Britton et al. (1998)</a> for information on fan plots, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/cytofan/readme/README.htm" target="_blank">README</a> for package usage.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/cytofan.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fingertipscharts" target="_blank">fingertipscharts</a> v0.0.1: Provides tools to recreate the visualizations that are displayed on the <a rel="nofollow" href="http://fingertips.phe.org.uk/" target="_blank">Fingertips</a> website of U.K. public health data. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/fingertipscharts/vignettes/quick_charts.html" target="_blank">vignette</a> explains how to use the package.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/fingertipscharts.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ggvoronoi" target="_blank">ggvoronoi</a> v0.8.0: Provides functions to create, manipulate and visualize Voronoi diagrams using the <a rel="nofollow" href="https://cran.r-project.org/package=deldir" target="_blank"><code>deldir</code></a> and <code>ggplot2</code> packages. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/ggvoronoi/vignettes/ggvoronoi.html" target="_blank">vignette</a> shows how.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-08-21-July-Top40_files/ggvoronoi.png?w=450&ssl=1" data-recalc-dims="1" height="450"></p>
<p> </p>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://rviews.rstudio.com/2018/08/27/july-2018-top-40-new-packages/"> R Views</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/u-nExPt8P60" height="1"></a></div>