<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/dec-2017-top-40-new-package-picks/"></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/2018/01/25/dec-2017-new-package-picks/"> R Views</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Sometimes it appears to me that the <em>invisible hand</em> economists speak of guides the <em>market</em> for new R packages. Eight of the 129 new packages that stuck to CRAN in December fall under <em>Computational Methods</em>, a category I have only recently begun using. All of them made it into the list below of my “Top 40” picks. One day, I would like to go back and reexamine the categories I have been using to see if package developers really do respond to some idea that is “in the air” or whether the variation in categories is just one more of my many hidden biases.</p>
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<h2>Computational Methods</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=alphashape3d" target="_blank">alphashape3d</a> v1.3: Provides functions to compute the alpha-shape (a generalization of the convex hull) of a finite set of points in the three-dimensional space.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/alphashape3d.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=deGradInfer" target="_blank">deGradInfer</a> v1.0.0: Implements efficient Bayesian parameter inference for systems of ordinary differential equations based on adaptive gradient matching. See <a rel="nofollow" href="http://proceedings.mlr.press/v31/dondelinger13a.pdf" target="_blank">Dondelinger et al. (2013)</a> and <a rel="nofollow" href="http://theses.gla.ac.uk/7987/1/2017macdonaldphd.pdf" target="_blank">Macdonald (2017)</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/deGradInfer/vignettes/ODE_parameter_inference.pdf" target="_blank">vignette</a> provides examples.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=FixedPoint" target="_blank">FixedPoint</a> v0.1: Provides algorithms for finding fixed-point vectors, including iterative procedures for non-linear integral equations <a rel="nofollow" href="doi:10.1145/321296.321305" target="_blank">Anderson (1965)</a>, epsilon extrapolation methods <a rel="nofollow" href="doi:10.2307/2004051" target="_blank">Wynn (1962)</a>, and minimal polynomial methods <a rel="nofollow" href="doi:10.1137/0713060" target="_blank">Cabay &amp; Jackson (1976)</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/FixedPoint/vignettes/FixedPoint.pdf" target="_blank">vignette</a> provides a very nice introduction to the subject.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=grapherator" target="_blank">grapherator</a> v1.0.0: Aimed at research in single- and multi-objective combinatorial optimization, the package provides functions for step-wise generation of weighted graphs. There are vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/grapherator/vignettes/introduction.html" target="_blank">Graph Generation</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/grapherator/vignettes/custom_generators.html" target="_blank">Custom Generators</a> and using <a rel="nofollow" href="https://cran.rstudio.com/web/packages/grapherator/vignettes/piping.html" target="_blank">pipes</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/grapherator.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=HMMEsolver" target="_blank">HMMEsolver</a> v0.1.0: Implements a fast solver for Henderson Mixed Model Equation via row operations without computing a matrix inverse. See <a rel="nofollow" href="arxiv:1710.09663" target="_blank">Kim (2017)</a> for more details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=kexpmv" target="_blank">kexpmv</a> v0.0.3: Implements functions from <a rel="nofollow" href="https://www.maths.uq.edu.au/expokit/" target="_blank">EXPOKIT</a> to calculate matrix exponentials. See <a rel="nofollow" href="doi:10.1145/285861.285868" target="_blank">Sidje RB, (1998)</a> for both small dense matrices and large sparse matrices based on Krylov subspace methods.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sparseEigen" target="_blank">sparseEigen</a> v0.1.0: Provides methods to compute sparse eigenvectors of a matrix with running time two to three orders of magnitude lower than existing methods. The methods are based on the paper by (<a rel="nofollow" href="http://ieeexplore.ieee.org/document/7558183/" target="_blank">Benidis et al. (2016)</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sparseEigen/vignettes/SparseEigenvectors.pdf" target="_blank">vignette</a> includes performance benchmarks.</p>
<p><img alt="Average Running Time" src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/sparseEigen.png?w=456&ssl=1" data-recalc-dims="1"> </p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=TukeyRegion" target="_blank">TukeyRegion</a> v0.1.2: Provides fast computation of Tukey regions, polytopes in the Euclidean space giving upper-level sets of the Tukey depth function for given data. For details, see <a rel="nofollow" href="arxiv:1412.5122" target="_blank">Liu, Mosler, and Mozharovskyi (2017)</a>.</p>
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<h2>Data</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=mlbgameday" target="_blank">mlbgameday</a> v0.0.1: Implements methods for multi-core processing of Gameday data from Major League Baseball Advanced Media (<a rel="nofollow" href="http://gd2.mlb.com/components/game/mlb/" target="_blank">http://gd2.mlb.com/components/game/mlb/</a>). There are vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mlbgameday/vignettes/database_connections.html" target="_blank">Database Connedtions</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mlbgameday/vignettes/parallel_processing.html" target="_blank">Parallel Processing</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mlbgameday/vignettes/pitch_plotting.html" target="_blank">Plotting Pitches</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mlbgameday/vignettes/search_games.html" target="_blank">Searching Games</a>.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/mlbgameday.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=robis" target="_blank">robis</a> v1.0.0: Implements a client for the Ocean Biogeographic Information System (<a rel="nofollow" href="http://iobis.org/" target="_blank">http://iobis.org</a>). See <a rel="nofollow" href="https://cran.rstudio.com/web/packages/robis/README.html" target="_blank">README</a> to get started.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=seaaroundus" target="_blank">seaaroundus</a> v1.2.0: Provides access to <a rel="nofollow" href="http://www.seaaroundus.org/" target="_blank">Sea Around Us</a> fish catch data. See <a rel="nofollow" href="https://cran.rstudio.com/web/packages/seaaroundus/README.html" target="_blank">README</a> to get started.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tidyhydat" target="_blank">tidyhydat</a> v0.3.2: Provides functions to extract historical and real-time national ‘hydrometric’ data from Water Survey of Canada data sources <a rel="nofollow" href="http://dd.weather.gc.ca/hydrometric/csv/" target="_blank">here</a> and <a rel="nofollow" href="http://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/" target="_blank">here</a>. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tidyhydat/vignettes/tidyhydat_an_introduction.html" target="_blank">Introduction</a> and an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tidyhydat/vignettes/tidyhydat_example_analysis.html" target="_blank">Example</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/tidyhydat.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=afCEC" target="_blank">afCEC</a> v1.0.2: Implements active function cross-entropy clustering partitions the n-dimensional data into the clusters by finding the parameters of the mixed generalized multivariate normal distribution, that optimally approximates the scattering of the data in the n-dimensional space. For details see <a rel="nofollow" href="doi:10.1016/j.eswa.2016.12.011" target="_blank">P. Spurek et al (2017)</a>.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/afCEC.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=dissever" target="_blank">dissever</a> v0.2-2: Enables spatial down-scaling of coarse-grid mapping to fine-grid mapping using predictive covariates and a model fitted using the ‘caret’ package. The original dissever algorithm was published by <a rel="nofollow" href="doi:10.1016/j.cageo.2011.08.021" target="_blank">Malone et al. (2012)</a> and extended by <a rel="nofollow" href="doi:10.1016/j.compag.2017.08.021" target="_blank">Roudier et al. (2017)</a>. There is a short <a rel="nofollow" href="https://cran.rstudio.com/web/packages/dissever/vignettes/dissever-demo.html" target="_blank">tutorial</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/dissever.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=mlapi" target="_blank">mlapi</a> Provides <code>R6</code> abstract classes for building machine-learning models with <a rel="nofollow" href="http://scikit-learn.org/" target="_blank"><code>scikit-learn</code></a>-like API. (<code>scikit-learn</code> is a popular module for <code>Python</code> programming language.) There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/mlapi/vignettes/developing-with-mlapi.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=Numero" target="_blank">Numero</a> v1.0.3: Implements an unsupervised statistical framework for defining subgroups in complex datasets based on visual cues <a rel="nofollow" href="doi:10.1021/pr201036j" target="_blank">Makinen et al. (2011)</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/Numero/vignettes/NumeroGuide.html" target="_blank">vignette</a> shows how to use the package.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/Numero.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=PPforest" target="_blank">PPforest</a> v0.1.0: Implements projection pursuit forest algorithm for supervised classification. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/PPforest/vignettes/PPforest-vignette.html" target="_blank">vignette</a> provides details.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/PPforest.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=qCBA" target="_blank">qCBA</a> v0.3.1: Implements quantitative classification by association rules. See <a rel="nofollow" href="arxiv:1711.10166" target="_blank">Kliegr (2017)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tfestimators" target="_blank">tfestimators</a> v1.5: Implements an interface to <code>TensorFlow</code> <a rel="nofollow" href="https://www.tensorflow.org/programmers_guide/estimators" target="_blank">Estimators</a>, a high-level API that provides implementations of many different model types, including linear models and deep neural networks. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/tensorflow_layers.html" target="_blank">Introduction</a> and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/creating_estimators.html" target="_blank">Custom Estimators</a>, the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/dataset_api.html" target="_blank">Dataset API</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/estimator_basics.html" target="_blank">Basic Components</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/feature_columns.html" target="_blank">Feature Columns</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/input_functions.html" target="_blank">Input Functions</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/parsing_spec.html" target="_blank">Parsing Utilites</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tfestimators/vignettes/run_hooks.html" target="_blank">Run Hooks</a>, and <a rel="nofollow" href="https://cran.rstudio.com/src/contrib/tfestimators_1.5.tar.gz" target="_blank">TensorBoard Visualization</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/tfestimators.png?w=456&ssl=1" data-recalc-dims="1">
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<h2>Science</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ePCR" target="_blank">ePCR</a> v0.9.9-4: Provides the top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the <a rel="nofollow" href="https://www.synapse.org/ProstateCancerChallenge" target="_blank">DREAM 9.5 mCRPC Prostate Cancer Challenge</a>. See <a rel="nofollow" href="doi:10.1016/S1470-2045(16)30560-5" target="_blank">Guinney J et al. (2017)</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=SimRVPedigree" target="_blank">simRVPedigree</a> v0.1.0: Provides routines to simulate and manipulate pedigrees ascertained to contain multiple family members affected by a rare disease. See <a rel="nofollow" href="doi:10.1101/234153" target="_blank">Nieuwoudt et al. (2017)</a> for the science and the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/SimRVPedigree/vignettes/SimRVPedigree.html" target="_blank">vignette</a> to get started.</p>
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<img src="https://i1.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/simrvpedigree.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=theseus" target="_blank">theseus</a> v0.1.0: Provides analysis and visualization tools for the interpretation of microbial community composition data, especially those originating from amplicon sequencing. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/theseus/vignettes/general_usage.html" target="_blank">vignette</a> describes how to use the package.</p>
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<img src="https://i0.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/theseus.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=ForecastComb" target="_blank">ForecastComb</a> v1.1: Provides geometric and regression-based forecast combination methods under a unified user interface for the packages <a rel="nofollow" href="https://cran.r-project.org/package=ForecastCombinations" target="_blank">ForecastCombinations</a> and <a rel="nofollow" href="https://cran.r-project.org/package=GeomComb" target="_blank">GeomComb</a>. For details see <a rel="nofollow" href="doi:10.1016/j.jeconom.2013.11.003" target="_blank">Hsiao C, Wan SK (2014)</a>, <a rel="nofollow" href="doi:10.1111/j.1468-0262.2007.00785.x" target="_blank">Hansen BE (2007)</a>, [Elliott G, Gargano A, Timmermann A (2013)](<a rel="nofollow" href="doi:10.1016/j.jeconom.2013.04.017" target="_blank">doi:10.1016/j.jeconom.2013.04.017</a>] and <a rel="nofollow" href="doi:10.1016/0169-2070(89)90012-5" target="_blank">Clemen RT (1989)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=hesim" target="_blank">hesim</a> v0.1.0: Provides functions to develop and analyze health-economic simulation models, including random sampling functions for probabilistic sensitivity analyses <a rel="nofollow" href="doi:10.1002/hec.985" target="_blank">Claxton et al. (2005)</a>, individual patient simulations <a rel="nofollow" href="doi:10.1002/hec.1148" target="_blank">Brennan et al. (2006)</a>, cost-effectiveness analysis <a rel="nofollow" href="http://journals.sagepub.com/doi/10.1177/0272989X06297393" target="_blank">Basu and Meltzer (2007)</a>, and <a rel="nofollow" href="doi:10.1371/journal.pmed.1001058" target="_blank">Ioannidis and Garber (2011)</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/hesim/vignettes/cea-vignette.html" target="_blank">vignette</a> provides an overview.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=PlackettLuce" target="_blank">PlackettLuce</a> v0.2-1: Implements a generalization of the model jointly attributed to <a rel="nofollow" href="http://dx.doi.org/10.2307/2346567" target="_blank">Plackett (1975)</a> and Luce (1959) for modelling rankings data. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/PlackettLuce/vignettes/Overview.html" target="_blank">vignette</a> introduces the model.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=PUlasso" target="_blank">PUlasso</a> v2.1: Implements an efficient algorithm for solving the Positive and Unlabeled problem in low- or high-dimensional settings with lasso or group lasso penalty. See <a rel="nofollow" href="arxiv:1711.08129" target="_blank">Hyebin 7 Raskutti (2017)</a> for details and the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/PUlasso/vignettes/PUlasso-vignette.html" target="_blank">vignette</a> for an introduction.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=recurse" target="_blank">recurse</a> v1.0.1: Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/recurse/vignettes/recurse.html" target="_blank">vignette</a> works through a case study of using the package to analyze revisitations in animal movement data.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/recurse.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=samplesizeCMH" target="_blank">samplesizeCMH</a> v0.0.0: Provides functions to calculate the power and sample size for Cochran-Mantel-Haenszel tests, and for working with probability, odds, relative risk, and odds ratio values. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/samplesizeCMH/vignettes/samplesizeCMH-introduction.html" target="_blank">Introduction to the Cochran-Mantal-Haenszel Test</a>, and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/samplesizeCMH/vignettes/samplesizeCMH-power.html" target="_blank">Power Calculations</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/samplesizeCMH/vignettes/samplesizeCMH-samplesize.html" target="_blank">Sample Size Calculations</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=skimr" target="_blank">skimr</a> v1.0.1: Provides a function to display summary statistics at the console. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/skimr/vignettes/Using_skimr.html" target="_blank">Introduction</a> and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/skimr/vignettes/Using_fonts.html" target="_blank">Fonts</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/skimr/vignettes/Supporting_additional_objects.html" target="_blank">defining summary objects</a>.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/skimr.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=ZOIP" target="_blank">ZOIP</a> v0.1: Implements the ZOIP (Zeros Ones Inflated Proportional), proportional data distribution inflated with zeros and/or ones. See <a rel="nofollow" href="doi:10.1016/0047-259X(91)90008-P" target="_blank">Jørgensen and Barndorff-Nielsen (1991)</a>, <a rel="nofollow" href="doi:10.1080/0266476042000214501" target="_blank">Ferrari and Cribari-Neto (2004)</a>, and <a rel="nofollow" href="doi:10.18637/jss.v023.i07" target="_blank">Rigby and Stasinopoulos (2005)</a> for details, and the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/ZOIP/vignettes/my-vignette.html" target="_blank">vignette</a> for a summary.</p>
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<h2>Time Series</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=OSTSC" target="_blank">OSTSC</a> v0.0.1: Provides functions for oversampling imbalanced univariate time series classification data using integrated Enhanced Structure Preserving Oversampling (ESPO) and Adaptive Synthetic (ADASYN) methods. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/OSTSC/vignettes/Over_Sampling_for_Time_Series_Classification.pdf" target="_blank">vignette</a> describes the method and provides examples.</p>
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<h2>Utilities</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=JuniperKernel" target="_blank">JuniperKernel</a> v1.2.2.0: Implements the <a rel="nofollow" href="http://jupyter.org/" target="_blank"><code>Jupyter</code></a> kernel for R, providing an interface to libraries that exist in the <code>Jupyter</code> ecosystem for building widgets, plotting, and more. Look <a rel="nofollow" href="https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92" target="_blank">here</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=labelVector" target="_blank">labelVector</a> v0.0.1: Supports labels for atomic vectors in a light-weight design that is suitable for use in other packages. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/labelVector/vignettes/labelVector.html" target="_blank">vignette</a> provides details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ncmeta" target="_blank">ncmeta</a> v0.0.1: Provides functions to extract metadata from <a rel="nofollow" href="https://www.unidata.ucar.edu/software/netcdf/" target="_blank">NetCDF</a> data sources, which can be files, file handles, or servers. The provides a framework for the in-development <a rel="nofollow" href="https://github.com/hypertidy/ncmeta" target="_blank">tidync project</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RPostgres" target="_blank">RPostgres</a> v1.0-4: Implements a fully <code>DBI</code>-compliant, <code>Rcpp</code>-backed interface to <a rel="nofollow" href="https://www.postgresql.org/" target="_blank">PostgreSQL</a>, an open-source relational database.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=swatches" target="_blank">swatches</a> v0.5.0: Provides functions to read and inspect Adobe Color (ACO), Adobe Swatch Exchange (ASE), GIMP Palette (GPL), OpenOffice palette (SOC) files, and KDE Palette (colors) files.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=styler" target="_blank">stylr</a> v1.0.0: Provides functions to pretty-print R code without changing the user’s formatting intent. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/styler/vignettes/introducing_styler.html" target="_blank">Introduction</a>, and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/styler/vignettes/customizing_styler.html" target="_blank">Customization</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/styler/vignettes/performance_improvements.html" target="_blank">Performance Improvements</a>.</p>
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<h2>Visualizations</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=BioCircos" target="_blank">BioCircos.png</a> v0.2.2: Implements interactive <a rel="nofollow" href="http://circos.ca/intro/genomic_data/" target="_blank">Circos</a>-like visualizations of genomic data, to map information such as genetic variants, genomic fusions, and aberrations to a circular genome. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/BioCircos/vignettes/BioCircos.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/2018-01-22-Rickert-Dec2017-Pkgs_files/BioCircos.png?w=456&ssl=1" data-recalc-dims="1">
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<p><a rel="nofollow" href="https://cran.r-project.org/package=cubing" target="_blank">cubing</a> v1.0-1: Provides functions for visualizing, animating, solving and analyzing the Rubik’s cube. See <a rel="nofollow" href="arxiv:0803.3435" target="_blank">Rokicki (2008)</a> for the underlying Kociemba solver. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/cubing/vignettes/cubingintro.pdf" target="_blank">vignette</a> shows how to get started.</p>
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<img src="https://i2.wp.com/rviews.rstudio.com/post/2018-01-22-Rickert-Dec2017-Pkgs_files/cubing.png?w=456&ssl=1" data-recalc-dims="1">
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<p> </p>
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