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(This article was first published on <strong><a href="https://rviews.rstudio.com/2018/11/29/october-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>One hundred eighty-five new packages made it to CRAN in October. Here are my picks for the “Top 40” in eight categories: Computational Methods, Data, Machine Learning, Medicine, Science, Statistics, Utilities, and Visualization.</p>
<h3>Computational Methods</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=compboost" target="_blank">compboost</a> v0.1.0: Provides a C++ implementation of component-wise boosting written to obtain high run-time performance and full memory control. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/compboost/vignettes/compboost.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RcppEnsmallen" target="_blank">RcppEnsmallen</a> v0.1.10.0.1: Implements an interface to the C++ based <a rel="nofollow" href="http://ensmallen.org/" target="_blank">Ensmallen</a> mathematical optimization library that provides a simple set of abstractions for writing an objective function to optimize. Optimizers include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SAMCpack" target="_blank">SAMpack</a> v0.1.1: Implements Stochastic Approximation Monte Carlo (SAMC) samplers capable of sampling from multimodal or doubly intractable distributions. See  for a complete introduction to the method, and the <a rel="nofollow" href="https://cran.r-project.org/package=SAMCpack" target="_blank">vignette</a> for an introduction to the package.</p>
<h3>Data</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=crimedata" target="_blank">crimedata</a> v0.1.0: Provides access to publicly available, police-recorded open crime data from large cities in the United States that are included in the <a rel="nofollow" href="https://osf.io/zyaqn/" target="_blank">Crime Open Database</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/web/packages/nasapower/index.html" target="_blank">nasapower</a> v1.02: Implements an interface to <a rel="nofollow" href="https://power.larc.nasa.gov/" target="_blank"><code>POWER</code> (Prediction Of Worldwide Energy Resource)</a>, NASA’s global meteorology, surface solar energy, and climatology data API. Look <a rel="nofollow" href="https://ropensci.github.io/nasapower/" target="_blank">here</a> for a quick start.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=wikisourcer" target="_blank">wikisourcer</a> v0.1.1: Provides access to public domain works from <a rel="nofollow" href="https://wikisource.org/" target="_blank">Wikisource</a>, a free library from the Wikimedia Foundation project. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/wikisourcer/vignettes/wikisourcer.html" target="_blank">vignette</a> for a package tutorial.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/wikisourcer.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=gcForest" target="_blank">gcForest</a> v0.2.7: Provides an API interface to the <a rel="nofollow" href="https://github.com/pylablanche/gcForest" target="_blank">Python implementation</a> of Deep Forest, an alternative to Deep Learning. The algorithm is described in , and there is a brief package <a rel="nofollow" href="https://cran.r-project.org/web/packages/gcForest/vignettes/gcForest-docs.html" target="_blank">tutorial</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=galgo" target="_blank">galgo</a> v1.4: Allows users to build multivariate predictive models from large data sets having a far larger number of features than samples, such as in functional genomics data sets. See  for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=MachineShop" target="_blank">MachineShop</a> v0.2.0: Provides a common interface for machine learning model fitting, prediction, performance assessment, and presentation of results. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/MachineShop/vignettes/Introduction.html" target="_blank">Introduction</a> and a note on <a rel="nofollow" href="https://cran.r-project.org/web/packages/MachineShop/vignettes/MLModels.html" target="_blank">Implementation Conventions</a>.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/MachineShop.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=mlflow" target="_blank">mlflow</a> v0.8.0: Provides an interface to , an open-source platform for the complete machine learning life cycle that supports installation, tracking experiments, running projects, and saving models.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sboost" target="_blank">sboost</a> v0.1.0: Provides a fast, C++-based implementation of Freund and Schapire’s Adaptive Boosting (AdaBoost) algorithm, and includes methods for classifier assessment, predictions, and cross-validation.</p>
<h3>Medicine</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=CoRpower" target="_blank">CoRpower</a> v1.0.0: Provides functions to calculate power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in <a rel="nofollow" href="https://www.ncbi.nlm.nih.gov/pubmed/27037797" target="_blank">Gilbert et al. (2016)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/CoRpower/vignettes/CoRpower.html" target="_blank">vignette</a> demonstrates the math.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=radtools" target="_blank">radtools</a> v1.0.0: Provides a collection of utilities for navigating medical image data in DICOM and NIfTI formats. An emphasis on metadata allows simple conversion of image metadata to familiar R data structures, such as lists and data frames. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/radtools/vignettes/radtools_usage.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/radtools.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rpact" target="_blank">rpact</a> v1.0.0: Provides functions for designing and analyzing confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by . Look <a rel="nofollow" href="https://www.rpact.org/" target="_blank">here</a> for an overview.</p>
<h3>Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ClimProjDiags" target="_blank">ClimProjDiags</a> v0.0.1: Provides functions for computing metrics and indices for climate analysis, comparing models, and combining them into ensembles. There are vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/ClimProjDiags/vignettes/anomaly_agreement.html" target="_blank">anomaly agreement</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/ClimProjDiags/vignettes/diurnaltemp.html" target="_blank">diurnal temperatures</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/ClimProjDiags/vignettes/extreme_indices.html" target="_blank">extreme indices</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/ClimProjDiags/vignettes/heatcoldwaves.html" target="_blank">heat and cold wave duration</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=DEVis" target="_blank">DEVis</a> v1.0.0: Provides a comprehensive tool set for data aggregation, visual analytics, exploratory analysis, and project management that builds upon the Bioconductor <a rel="nofollow" href="http://bioconductor.org/packages/release/bioc/html/DESeq2.html" target="_blank">DESeq2</a> differential expression package. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/DEVis/vignettes/DEVis_vignette.pdf" target="_blank">vignette</a> offers a comprehensive introduction.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/DEVis.png?w=400&ssl=1" data-recalc-dims="1" height="300"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=epimdr" target="_blank">epimdr</a> v0.6-1: Provides functions for studying epidemics, including the <a rel="nofollow" href="http://www.public.asu.edu/~hnesse/classes/seir.html" target="_blank">S(E)IR model</a>, time-series SIR and chain-binomial stochastic models, catalytic disease models, and coupled map lattice models. It is a companion to the book <a rel="nofollow" href="https://www.springer.com/gp/book/9783319974866" target="_blank">Epidemics: Models and Data in R</a> and the Coursera course <a rel="nofollow" href="https://www.coursera.org/learn/epidemics" target="_blank">Epidemics Massive Online Open Course</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=firebehavioR" target="_blank">firebehavior</a> v0.1.1: Implements fire behavior prediction models, including those documented in  and . The <a rel="nofollow" href="https://cran.r-project.org/web/packages/firebehavioR/vignettes/firebehavioR.html" target="_blank">vignette</a> is informative.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/firebehavioR.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=lorentz" target="_blank">lorentz</a> v1.0.0: Provides the functionality to work with Lorentz transforms and the gyrogroup structure in <a rel="nofollow" href="https://en.wikipedia.org/wiki/Special_relativity" target="_blank">Special Relativity</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/lorentz.png?w=400&ssl=1" data-recalc-dims="1" height="300"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pubchunks" target="_blank">pubchunks</a> v0.1.0: Provides functions for extracting chunks of XML from scholarly articles without having to know how to work with XML. See <a rel="nofollow" href="https://cran.r-project.org/web/packages/pubchunks/readme/README.html" target="_blank">README</a> to get going.</p>
<h3>Statistics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=BayesMallows" target="_blank">BayesMallows</a> v0.1.1: Implements the Bayesian version of the Mallows rank model (Vitelli et al. (2018)(<a rel="nofollow" href="http://jmlr.org/papers/v18/15-481.html" target="_blank">http://jmlr.org/papers/v18/15-481.html</a>). The <a rel="nofollow" href="https://cran.r-project.org/web/packages/BayesMallows/vignettes/BayesMallowsPackage.html" target="_blank">vignette</a> provides the details.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/BayesMallows.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=contextual" target="_blank">contextual</a> v0.9.1: Facilitates the simulation and evaluation of context-free and contextual multi-Armed Bandit policies or algorithms to ease the implementation, evaluation, and dissemination of both existing and new bandit algorithms and policies. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/contextual/vignettes/contextual.html" target="_blank">Getting Started Guide</a> and this <a rel="nofollow" href="https://cran.r-project.org/web/packages/contextual/vignettes/posts.html" target="_blank">list of posts</a> for more information.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/contextual.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=coxrt" target="_blank">coxrt</a> v1.0.0: Implements Cox Proportional Hazards regression for right-truncated data. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/coxrt/vignettes/coxrt-vignette.html" target="_blank">vignette</a> gives the details.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/coxrt.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=crossrun" target="_blank">crossrun</a> v0.1.0: Estimates the joint distribution of number of crossings and the longest run in a series of independent Bernoulli trials. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/crossrun/vignettes/vignettecrossrun.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=logisticRR" target="_blank">logisticRR</a> v0.2.0: Asserting that relative risk is often of interest in public health, this package provides functions to return adjusted relative risks from logistic regression model under potential confounders. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/logisticRR/vignettes/logisticRR.html" target="_blank">vignette</a> does the math.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=lognorm" target="_blank">lognorm</a> v0.1.3: Estimates the distribution parameters and computes moments and other basic statistics of the lognormal distribution , and also provides an approximation to the distribution of the sum of several correlated lognormally distributed variables . There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/lognorm/vignettes/aggregateCorrelated.html" target="_blank">Aggregating Correlated Random Variables</a> and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/lognorm/vignettes/lognormalSum.html" target="_blank">Approximating Sums</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=lolog" target="_blank">lolog</a> v1.1: Provides functions to estimate Latent Order Logistic (LOLOG) Models for Networks, and also visual diagnostics and goodness of fit metrics are provided. See  for a detailed description of the methods. One vignette works through an <a rel="nofollow" href="https://cran.r-project.org/web/packages/lolog/vignettes/lolog-ergm.pdf" target="_blank">example</a>, and another introduces <a rel="nofollow" href="https://cran.r-project.org/web/packages/lolog/vignettes/lolog-introduction.pdf" target="_blank">lolog models</a>.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/lolog.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=matrixNormal" target="_blank">matrixNormal</a> v0.0.0: Provides the functions to compute densities, probabilities, and random deviates of the Matrix Normal distribution. See </p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=outcomerate" target="_blank">outcomerate</a> v1.0.1: Implements standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate that allow researchers to measure the quality of survey data using standards published by the <a rel="nofollow" href="https://www.aapor.org/" target="_blank">American Association of Public Opinion Research</a>. For details, see <a rel="nofollow" href="https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx" target="_blank">AAPOR (2016)</a>. The vignette provides an <a rel="nofollow" href="https://cran.r-project.org/web/packages/outcomerate/vignettes/intro-to-outcomerate.html" target="_blank">Introduction</a>.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/outcomerate.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=parmsurvfit" target="_blank">parmsurvfit</a> v0.0.1: Fits right-censored data to a given parametric distribution, and produces summary statistics, hazard, cumulative hazard and probability plots, and the Anderson-Darling test statistic. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/parmsurvfit/vignettes/parmsurvfit_vig.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ppgmmga" target="_blank">ppgmmga</a> v1.0.1: Implements a Projection Pursuit algorithm for dimension reduction based on Gaussian Mixture Models. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/ppgmmga/vignettes/ppgmmga.html" target="_blank">vignette</a> provides a quick tour of the package.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/ppgmmga.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RcppDist" target="_blank">RcppDist</a> v0.1.1: Provides additional statistical distributions that<br>
can be called from C++ when writing code using Rcpp or RcppArmadillo. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/RcppDist/vignettes/RcppDist.pdf" target="_blank">vignette</a> for a list of the distributions supported.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=simstandard" target="_blank">simstandard</a> v0.2.0: Enables the creation of simulated data from structural equation models with standardized loading. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/simstandard/vignettes/simstandard_tutorial.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/simstandard.png?w=400&ssl=1" data-recalc-dims="1" height="300"></p>
<h3>Utilities</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=carrier" target="_blank">carrier</a> v0.1.0: Enables users to create functions that are isolated from their environment. These isolated functions, also called crates, print at the console with their total size and can be easily tested locally before being sent to a remote.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=carbonate" target="_blank">carbonate</a> v0.1.0: Implements an interface to <a rel="nofollow" href="https://carbon.now.sh/about" target="_blank">carbon.js</a>, which allows developers to create images of source code. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/carbonate/vignettes/tests_and_coverage.html" target="_blank">Tests and Coverage</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=generics" target="_blank">generics</a> v0.0.1: In order to reduce potential package dependencies and conflicts, <code>generics</code> provides a number of commonly used S3 generics.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=REPLesentR" target="_blank">REPLesentR</a> v0.3.0: Allows users to create presentations and display them inside the R <code>REPL</code> (console). Supports <code>RMarkdown</code> and other text format.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=stationery" target="_blank">stationery</a> v0.98.5.5: Provides templates, guides, and scripts for writing documents in <code>LaTeX</code> and <code>R markdown</code> to produce guides, slides, and reports; and includes several vignettes to assist new users of literate programming. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/stationery/vignettes/stationery.pdf" target="_blank">Overview</a>, a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/stationery/vignettes/Rmarkdown.pdf" target="_blank">R Markdown Basics</a>, and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/stationery/vignettes/HTML_special_features.html" target="_blank">R Markdown HTML</a>, and a comparison between <a rel="nofollow" href="https://cran.r-project.org/web/packages/stationery/vignettes/code_chunks.pdf" target="_blank">Sweave and Knitr code chunks</a>.</p>
<h3>Visualization</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=balance" target="_blank">balance</a> v0.1.6: Provides an alternative scheme for visualizing balances (used in <a rel="nofollow" href="https://en.wikipedia.org/wiki/Compositional_data" target="_blank">compositional data analysis</a>) as described in , as well as a method for principal balance analysis. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/balance/vignettes/balance.html" target="_blank">vignette</a> for details.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/balances.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=trelliscopejs" target="_blank">trelliscopejs</a> v0.1.14: Provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS, including high-level functions for creating displays from within <code>dplyr</code> or <code>ggplot2</code> workflows. There is a vignette on <a rel="nofollow" href="https://hafen.github.io/trelliscopejs/#trelliscope" target="_blank">trelliscope Documentation</a> and a <a rel="nofollow" href="https://cran.r-project.org/web/packages/trelliscopejs/vignettes/rd.html" target="_blank">trelliscope Package Reference</a>.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-11-19-Rickert-OctTop40_files/trelliscopejs.png?w=450&ssl=1" data-recalc-dims="1" height="400"></p>
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