<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/october-2017-new-packages/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="https://rviews.rstudio.com/2017/11/22/october-2017-new-packages/"> R Views</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Of the 182 new packages that made it to CRAN in October, here are my picks for the “Top 40”. They are organized into eight categories: Engineering, Machine Learning, Numerical Methods, Science, Statistics, Time Series, Utilities and Visualizations. Engineering is a new category, and its appearance may be an early signal for the expansion of R into a new domain. The Science category is well-represented this month. I think this is the result of the continuing trend for working scientists to wrap their specialized analyses into R packages.</p>
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<h2>Engineering</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=FlowRegEnvCost" target="_blank">FlowRegEnvCost</a> 0.1.1: Calculates the daily environmental costs of river-flow regulation by dams based on <a rel="nofollow" href="doi:10.1007/s11269-017-1663-0" target="_blank">García de Jalon et al. (2017)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rroad" target="_blank">rroad</a> v0.0.4: Computes and visualizes the International Roughness Index (IRI) given a longitudinal road profile for a single road segment, or for a sequence of segments with a fixed length. For details on The International Road Roughness Experiment establishing a correlation and a calibration standard for measurements, see the <a rel="nofollow" href="http://documents.worldbank.org/curated/en/326081468740204115" target="_blank">World Bank technical paper</a>. The vignette shows an example of a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/rroad/vignettes/RoadFeatures.html" target="_blank">Road Condition Analysis</a>. The following <code>scaleogram</code> was produced from a continuous wavelet transform of a 3D accelerometer signal.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/rroad.png?w=456&ssl=1" data-recalc-dims="1">
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<h2>Machine Learning</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=detrendr" target="_blank">detrendr</a> v0.1.0: Implements a method based on an algorithm by <a rel="nofollow" href="http://dx.doi.org/10.1093/bioinformatics/btx434" target="_blank">Nolan et al. (2017)</a> for detrending images affected by bleaching. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/detrendr/vignettes/detrendr.html" target="_blank">vignette</a></p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/detrendr.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=MlBayesOpt" target="_blank">MlBayesOpt</a> v0.3.3: Provides a framework for using Bayesian optimization (see <a rel="nofollow" href="doi:10.1109/JPROC.2015.2494218" target="_blank">Shahriari et al.</a> to tune hyperparameters for support vector machine, random forest, and <a rel="nofollow" href="doi:10.1145/2939672.2939785" target="_blank">extreme gradient boosting</a> models. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/MlBayesOpt/vignettes/MlBayesOpt.html" target="_blank">vignette</a> shows how to set things up.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rerf" target="_blank">rerf</a> v1.0: Implements an algorithm, Random Forester (RerF), developed by <a rel="nofollow" href="arxiv:1506.03410v2" target="_blank">Tomita (2016)</a>, which is similar to the Random Combination (Forest-RC) algorithm developed by <a rel="nofollow" href="doi:10.1023/A:1010933404324" target="_blank">Breiman (2001)</a>. Both algorithms form splits using linear combinations of coordinates.</p>
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<h2>Numerical Methods</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=episode" target="_blank">episode</a> v1.0.0: Provides statistical tools for inferring unknown parameters in continuous time processes governed by ordinary differential equations (ODE). See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/episode/vignettes/episode.html" target="_blank">Introduction</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=KGode" target="_blank">KGode</a> v1.0.1: Implements the kernel ridge regression and the gradient matching algorithm proposed in <a rel="nofollow" href="http://jmlr.org/proceedings/papers/v48/niu16.html" target="_blank">Niu et al. (2016)</a>, and the warping algorithm proposed in <a rel="nofollow" href="doi:10.1007/s00180-017-0753-z" target="_blank">Niu et al. (2017)</a> for improving parameter estimation in ODEs.</p>
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<h2>Science</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=adjclust" target="_blank">adjclust</a> v0.5.2: Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated with a position, and only adjacent clusters can be merged. The algorithm, which is time- and memory-efficient, is described in <a rel="nofollow" href="https://hal.archives-ouvertes.fr/tel-01288568v1" target="_blank">Alia Dehman (2015)</a>. There are vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/adjclust/vignettes/hicClust.html" target="_blank">Clustering Hi-C Contact Maps</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/adjclust/vignettes/notesCHAC.html" target="_blank">Implementation Notes</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/adjclust/vignettes/snpClust.html" target="_blank">Inferring Linkage Disequilibrium blocks from Genotypes</a>.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/adjclust.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=hsdar" target="_blank">hsdar</a> v0.6.0: Provides functions for transforming reflectance spectra, calculating vegetation indices and red edge parameters, and spectral resampling for hyperspectral remote sensing and simulation. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/hsdar/vignettes/Hsdar-intro.pdf" target="_blank">Introduction</a> offers several examples.</p>
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<p><a rel="nofollow" href="https://cran.r-project.org/package=mapfuser" target="_blank">mapfuser</a> v0.1.2: Constructs consensus genetic maps with LPmerge (See <a rel="nofollow" href="doi:10.1093/bioinformatics/btu091" target="_blank">Endelman and Plomion (2014)</a>) and models the relationship between physical distance and genetic distance using thin-plate regression splines (see <a rel="nofollow" href="doi:10.1111/1467-9868.00374" target="_blank">Wood (2003)</a>). The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mapfuser/vignettes/mapfuser.html" target="_blank">vignette</a> explains how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=mortAAR" target="_blank">mortAAR</a> v1.0.0: Provides functions for the analysis of archaeological mortality data <a rel="nofollow" href="https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false" target="_blank">See Chamberlain (2006)</a>. There is a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mortAAR/vignettes/mortAAR_vignette-1.html" target="_blank">Lifetables</a> and an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mortAAR/vignettes/mortAAR_vignette_extended.html" target="_blank">Extended Discussion</a>.</p>
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<p><a rel="nofollow" href="https://cran.r-project.org/package=skyscapeR" target="_blank">skyscapeR</a> v0.2.2: Provides a tool set for data reduction, visualization and analysis in skyscape archaeology, archaeoastronomy and cultural astronomy. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/skyscapeR/vignettes/skyscapeR.html" target="_blank">vignette</a> shows how to use the package.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/skyscapeR.png?w=456&ssl=1" data-recalc-dims="1">
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<h2>Statistics</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=BayesRS" target="_blank">BayesRS</a> v0.1.2: Fits hierarchical linear Bayesian models, samples from the posterior distributions of model parameters in <a rel="nofollow" href="http://mcmc-jags.sourceforge.net/" target="_blank">JAGS</a>, and computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio ([See <a rel="nofollow" href="doi:10.3758/PBR.16.4.752" target="_blank">Wetzels et al.(2009)</a>. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/BayesRS/" target="_blank">Introduction</a>.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/BayesRS.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=CatPredi" target="_blank">CatPredi</a> v1.1: Allows users to categorize a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximizing the discriminative ability of the model. See <a rel="nofollow" href="doi:10.1177/0962280215601873" target="_blank">Barrio et al. (2015)</a> and <a rel="nofollow" href="https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf" target="_blank">Barrio et al. (2017)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=CovTools" target="_blank">CovTools</a> v0.2.1: Provides a collection of geometric and inferential tools for convenient analysis of covariance structures. For an introduction to covariance in multivariate statistical analysis, see <a rel="nofollow" href="doi:10.1214/ss/1177013111" target="_blank">Schervish (1987)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=genlogis" target="_blank">genlogis</a> v0.5.0: Provides basic distribution functions for a generalized logistic distribution proposed by <a rel="nofollow" href="https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf" target="_blank">Rathie and Swamee (2006)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=emmeans" target="_blank">emmeans</a> v0.9.1: Provides functions to obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models, and computes contrasts or linear functions of EMMs, trends, and comparisons of slopes. There are twelve vignettes including <a rel="nofollow" href="https://cran.rstudio.com/web/packages/emmeans/vignettes/basics.html" target="_blank">The Basics</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/emmeans/vignettes/comparisons.html" target="_blank">Comparisons and Contrasts</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/emmeans/vignettes/confidence-intervals.html" target="_blank">Confidence Intervals and Tests</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/emmeans/vignettes/interactions.html" target="_blank">Interaction Analysis</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/emmeans/vignettes/messy-data.html" target="_blank">Working with Messy Data</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/emmeans.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=ESTER" target="_blank">ESTER</a> v0.1.0: Provides an implementation of sequential testing that uses evidence ratios computed from the Akaike weights of a set of models. For details see <a rel="nofollow" href="doi:10.1177/0049124104268644" target="_blank">Burnham &amp; Anderson (2004)</a>. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/ESTER/vignettes/ESTER.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=FarmTest" target="_blank">FarmTest</a> v1.0.0: Provides functions to perform robust multiple testing for means in the presence of latent factors. It uses Huber’s loss function to estimate distribution parameters and accounts for strong dependence among coordinates via an approximate factor model. See <a rel="nofollow" href="https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxzdGV2ZXdlbnhpbnp8Z3g6ZThlYTc0YjY3MDU1NGRk" target="_blank">Zhou et al.(2017)</a> for details. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/FarmTest/vignettes/farmtest-vignette.html" target="_blank">vignette</a> to get you started.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/FarmTest.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=miic" target="_blank">miic</a> v0.1: Implements an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. For more information see <a rel="nofollow" href="doi:10.1371/journal.pcbi.1005662" target="_blank">Verny et al. (2017)</a>.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2017-11-17-Oct-New-pkgs_files/miic.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=modcmfitr" target="_blank">modcmfitr</a> v0.1.0: Fits a modified version of the Connor-Mosimann distribution ( <a rel="nofollow" href="doi:10.2307/2283728" target="_blank">Connor &amp; Mosimann (1969)</a>), a Connor-Mosimann distribution, or a Dirichlet distribution to elicited quantiles of a multinomial distribution. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/modcmfitr/vignettes/modcmfitrOverview.pdf" target="_blank">vignette</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pense" target="_blank">pense</a> v1.0.8: Provides a robust penalized elastic net S and MM estimator for linear regression as described in <a rel="nofollow" href="https://gcohenfr.github.io/pdfs/PENSE_manuscript.pdf" target="_blank">Freue et al. (2017)</a>.</p>
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<p><a rel="nofollow" href="https://cran.r-project.org/package=paramtest" target="_blank">paramtest</a> v0.1.0: Enables running simulations or other functions while easily varying parameters from one iteration to the next. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/paramtest/vignettes/Simulating-Power.html" target="_blank">vignette</a> shows how to run a power simulation.</p>
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<p><a rel="nofollow" href="https://cran.r-project.org/package=rENA" target="_blank">rENA</a> v0.1.0: Implements functions to perform epistemic network analysis <a rel="nofollow" href="http://dx.doi.org/10.18608/jla.2016.33.3" target="_blank">ENA</a>, a novel method for identifying and quantifying connections among elements in coded data, and representing them in dynamic network models, which illustrate the structure of connections and measure the strength of association among elements in a network.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rma.exact" target="_blank">rma.exact</a> v0.1.0: Provides functions to compute an exact CI for the population mean under a random-effects model. For details, see <a rel="nofollow" href="https://haben-michael.github.io/research/Exact_Inference_Meta.pdf" target="_blank">Michael, Thronton, Xie, and Tian (2017)</a>.</p>
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<h2>Time Series</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=carfima" target="_blank">carfima</a> v1.0.1: Provides a toolbox to fit a continuous-time, fractionally integrated ARMA process <a rel="nofollow" href="https://arxiv.org/pdf/0902.1403.pdf" target="_blank">CARFIMA</a> on univariate and irregularly spaced time-series data using a general-order CARFIMA(p, H, q) model for p>q as specified in <a rel="nofollow" href="doi:10.1111/j.1467-9868.2005.00522.x" target="_blank">Tsai and Chan (2005)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=colorednoise" target="_blank">colorednoise</a> v0.0.1: Provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation) based on work by <a rel="nofollow" href="doi:10.1016/j.tree.2009.04.009" target="_blank">Ruokolainen et al. (2009)</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/colorednoise/vignettes/noise.html" target="_blank">vignette</a> describes colored noise.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=nnfor" target="_blank">nnfor</a> v0.9: Provides functions to facilitate automatic time-series modelling with neural networks. Look <a rel="nofollow" href="http://kourentzes.com/forecasting/2017/02/10/forecasting-time-series-with-neural-networks-in-r/" target="_blank">here</a> for some help getting started.</p>
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<h2>Utilities</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=hdf5r" target="_blank">hdf5r</a> v1.0.0: Provides an object-oriented wrapper for the <a rel="nofollow" href="https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html" target="_blank">HDF5</a> API using R6 classes. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/hdf5r/vignettes/hdf5r.html" target="_blank">vignette</a> shows how to use the package.</p>
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<p><a rel="nofollow" href="https://cran.r-project.org/package=geoops" target="_blank">geoops</a> v0.1.2: Provides tools for working with the <a rel="nofollow" href="https://tools.ietf.org/html/rfc7946" target="_blank">GeoJSON</a> geospatial data interchange format. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/geoops/vignettes/geoops_vignette.html" target="_blank">Introduction</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=linl" target="_blank">linl</a> v0.0.2: Adds a <code>LaTeX</code> Letter class to <code>rmarkdown</code>, using the <code>pandoc-letter</code> template adapted for use with <code>markdown</code>. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/linl/vignettes/linl.pdf" target="_blank">vignette</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/linl/README.html" target="_blank">README</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=oshka" target="_blank">oshka</a> v0.1.2: Expands quoted language by recursively replacing any symbol that points to quoted language with the language itself. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/oshka/vignettes/introduction.html" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/oshka/vignettes/nse-fun.html" target="_blank">Non Standard Evaluation Functions</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rcreds" target="_blank">rcreds</a> v0.6.6: Provides functions to read and write credentials to and from an encrypted file. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/rcreds/vignettes/rcreds.html" target="_blank">vignette</a> describes how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RMariaDB" target="_blank">RMariaDB</a> v1.0-2: Implements a DBI-compliant interface to <a rel="nofollow" href="https://mariadb.org/" target="_blank">MariaDB</a> and <a rel="nofollow" href="https://www.mysql.com/" target="_blank">MySQL</a> databases.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=securitytxt" target="_blank">securitytxt</a> v0.1.0: Provides tools to identify and parse <code>security.txt</code> files to enable the analysis and adoption of the <a rel="nofollow" href="https://tools.ietf.org/html/draft-foudil-securitytxt-00" target="_blank">Web Security Policies</a> draft standard.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=usethis" target="_blank">usethis</a> v1.1.0: Automates package and project setup tasks, including setting up unit testing, test coverage, continuous integration, Git, GitHub, licenses, <code>Rcpp</code>, RStudio projects, and more that would otherwise be performed manually. <a rel="nofollow" href="https://cran.rstudio.com/web/packages/usethis/README.html" target="_blank">README</a> provides examples.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=xltabr" target="_blank">xltabr</a> v0.1.1: Provides functions to produce nicely formatted cross tabulations to Excel using [openxlsx]((<a rel="nofollow" href="https://cran.r-project.org/package=openxlsx" target="_blank">https://cran.r-project.org/package=openxlsx</a>), which has been developed to help automate the process of publishing Official Statistics. Look <a rel="nofollow" href="https://github.com/moj-analytical-services/xltabr" target="_blank">here</a> for documentation.</p>
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<h2>Visualizations</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=iheatmapr" target="_blank">iheatmapr</a> v0.4.2: Provides a system for making complex, interactive heatmaps. Look at the <a rel="nofollow" href="https://ropensci.github.io/iheatmapr/index.html" target="_blank">webpage</a> for examples.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=otvPlots" target="_blank">otvPlots</a> v0.2.0: Provides functions to automate the visualization of variable distributions over time, and compute time-aggregated summary statistics for large datasets. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/otvPlots/README.html" target="_blank">README</a> for an introduction.</p>
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