<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/jan-2018-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/2018/02/22/jan-2018-top-40-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>Here are my “Top 40” picks from the two hundred or so new packages that stuck to CRAN in January, listed under seven categories: Data, Data Science, Science, Statistics, Time Series, Utilities and Visualizations (I say “stuck to” because I counted at least six packages that were accepted onto CRAN in January but removed within the month. Having packages quickly removed from CRAN is a phenomenon I have observed in recent months.)</p>
<p>While looking over the packages that I have listed under Data and Science, it struck me that in addition to being the world’s largest repository of statistical knowledge, CRAN is becoming a repository for practical, hard-won scientific knowledge.</p>
<h2>Data</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=cancensus" target="_blank">cancensus</a> v0.1.7: Provides an interface to Canadian census and geographic data using the <a rel="nofollow" href="https://censusmapper.ca/api" target="_blank">CensusMapper</a> API. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/cancensus/vignettes/cancensus.html" target="_blank">Introduction</a> and a vignette for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/cancensus/vignettes/Making_maps_with_cancensus.html" target="_blank">Making maps</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=elevatr" target="_blank">elevatr</a> v0.1.4: Provides access to several services offering elevation data, and returns the data either as a SpatialPointsDataFrame from point elevation services or as a raster object from raster elevation services. Currently, the package supports access to the <a rel="nofollow" href="https://mapzen.com/documentation/elevation/elevation-service/" target="_blank">Mapzen Elevation Service</a>, <a rel="nofollow" href="https://mapzen.com/documentation/terrain-tiles/" target="_blank">Mapzen Terrain Service</a>, <a rel="nofollow" href="https://aws.amazon.com/public-datasets/terrain/" target="_blank">Amazon Web Services Terrain Tiles</a>, and the <a rel="nofollow" href="http://ned.usgs.gov/epqs/" target="_blank">USGS Elevation Point Query Service</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/elevatr/vignettes/introduction_to_elevatr.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fabricatr" target="_blank">fabricatr</a> v0.2.0: Provides functions to simulate hierarchical and correlated data. There are several vignettes including a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/fabricatr/vignettes/getting_started.html" target="_blank">Getting Started</a> guide and an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/fabricatr/vignettes/advanced_features.html" target="_blank">Advanced Features</a> guide, as well as introductions to <a rel="nofollow" href="https://cran.rstudio.com/web/packages/fabricatr/vignettes/resampling.html" target="_blank">Resampling</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/fabricatr/vignettes/variable_generation.html" target="_blank">Generating Discrete Random Variables</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=getTBinR" target="_blank">getTBinR</a> v0.5.2: Facilitates easy import of analysis-ready World Health Organisation Tuberculosis data, and provides plotting functions for exploratory data analysis. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/getTBinR/vignettes/case_study_global_trends.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/getTBinR.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=homologene" target="_blank">homologene</a> v1.1.68: Provides a wrapper for the <a rel="nofollow" target="_blank">homologene database</a> by the National Center for Biotechnology Information, which allows searching for gene homologs across species.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=photobiologyFilters" target="_blank">photobiologyFilters</a> v0.4.4: Is a data-only package with spectral ‘transmittance’ data for frequently used filters and materials, including plastic sheets and films, optical glass and ordinary glass, and some labware. It complements the <a rel="nofollow" href="https://cran.r-project.org/package=photobiology" target="_blank">photobiology</a> package. See this <a rel="nofollow" href="http://www.r4photobiology.info/" target="_blank">website</a> and the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/photobiologyFilters/vignettes/user-guide.html" target="_blank">vignette</a> for details.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/photobiologyFilters.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tfdatasets" target="_blank">tfdatasets</a> v1.5: Provides an interface to <a rel="nofollow" href="https://www.tensorflow.org/programmers_guide/datasets" target="_blank"><code>TensorFlow</code> Datasets</a>, a high-level library for building complex input pipelines from simple, re-usable pieces.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=washdata" target="_blank">washdata</a> v0.1.2: Provides access to the urban water and sanitation survey data set collected by <a rel="nofollow" href="https://www.wsup.com/" target="_blank">Water and Sanitation for the Urban Poor (WSUP)</a>, with technical support from Valid International. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/washdata/vignettes/washdata.html" target="_blank">vignette</a>.</p>
<h2>Data Science</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=CRPClustering" target="_blank">CRPClustering</a> v1.0: Provides a clustering method using the Chinese restaurant process <a rel="nofollow" href="doi:10.1007/BF01213386" target="_blank">Pitman (1995)</a> that does not need to decide the number of clusters in advance. Also provides functions to calculate the ambiguity of clusters as entropy <a rel="nofollow" href="doi:10.1016/S0370-1573(98)00082-9" target="_blank">Yngvason (1999)</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/CRPClustering/vignettes/CRPClustering-vignette.pdf" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=kerasformula" target="_blank">kerasformula</a> v0.1.1: Provides a high-level interface for <code>keras</code> neural nets. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/kerasformula/vignettes/kerasformula.html" target="_blank">vignette</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=multiROC" target="_blank">multiROC</a> v1.0.0: Provides tools to solve problems with multiple classes by computing the areas under ROC curve via micro- and macro-averaging. The methodology is described in <a rel="nofollow" href="https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf" target="_blank">Van Asch (2013)</a> and <a rel="nofollow" href="http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html" target="_blank">Pedregosa et al. (2011)</a>. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/multiROC/vignettes/my-vignette.html" target="_blank">vignette</a> for a quick tour.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/multiROC.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=reinforcelearn" target="_blank">reinforcelearn</a> v0.1.0: Implements reinforcement learning environments and algorithms. as described in <a rel="nofollow" href="https://www.cambridge.org/core/journals/robotica/article/reinforcement-learning-an-introduction-by-richard-s-sutton-and-andrew-g-barto-adaptive-computation-and-machine-learning-series-mit-press-bradford-book-cambridge-mass-1998-xviii-322-pp-isbn-0262193981-hardback-3195/176DB49A1247A53B75B81EFCF32CA157" target="_blank">Sutton &amp; Barto (1998)</a>. There are vignettes for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/reinforcelearn/vignettes/agents.html" target="_blank">Agents</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/reinforcelearn/vignettes/environments.html" target="_blank">Environments</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=stranger" target="_blank">stranger</a> v0.3.2: Provides a framework for unsupervised anomalies detection There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/stranger/vignettes/stranger_for_the_impatient.html" target="_blank">Vignette for the Impatient</a>, and vignettes for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/stranger/vignettes/stranger_weirds_methods.html" target="_blank">Methods</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/stranger/vignettes/working_with_weirds.html" target="_blank">Anomalies manual selection</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tidypredict" target="_blank">tidypredict</a> v0.1.0: Provides functions to parse a fitted ‘R’ model object, and return a SQL query. There are vignettes for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tidypredict/vignettes/glm.html" target="_blank">GLM</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tidypredict/vignettes/randomForest.html" target="_blank">Random Forest</a> models.</p>
<h2>Science</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=annovarR" target="_blank">annovarR</a> v1.0.0: Provides unctions and database resources to offer an integrated framework to annotate genetic variants from genome and transcriptome data. The wrapper functions unify the interface of many published annotation tools, such as <a rel="nofollow" href="http://asia.ensembl.org/info/docs/tools/vep/index.html" target="_blank">VEP</a>, <a rel="nofollow" href="http://annovar.openbioinformatics.org/" target="_blank">ANNOVAR</a>, <a rel="nofollow" href="https://github.com/brentp/vcfanno" target="_blank">vcfanno</a>, and <a rel="nofollow" href="http://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.html" target="_blank">AnnotationDbi</a>. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/annovarR/vignettes/introduction_to_annovarR.html" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/annovarR/vignettes/databases_in_annovarR.html" target="_blank">Databases</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pubh" target="_blank">pubh</a> v0.1.7: Offers a toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/pubh/vignettes/introduction.html" target="_blank">Introduction</a> and a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/pubh/vignettes/regression.html" target="_blank">Regression Example</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=trajr" target="_blank">trajr</a> v1.0.0: Provides a toolbox to assist with statistical analysis of two-dimensional animal trajectories. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/trajr/vignettes/trajr-vignette.html" target="_blank">vignette</a> provides several examples.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/trajr.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h2>Statistics</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dalmatian" target="_blank">dalmatian</a> v0.3.0: Automates fitting a double GLM in <code>JAGS</code>. There is a vignette on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/dalmatian/vignettes/weights-1-simulate.html" target="_blank">weighted regression</a> and a two-part example using the Pied Flycatcher Data: <a rel="nofollow" href="https://cran.rstudio.com/web/packages/dalmatian/vignettes/pied-flycatchers-1.html" target="_blank">Part 1</a> and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/dalmatian/vignettes/pied-flycatchers-2.html" target="_blank">Part 2</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=dirichletprocess" target="_blank">dirichletprocess</a> v0.2.0: Enables the creation of Dirichlet process objects that can be used as infinite mixture models. Examples include density estimation, Poisson process intensity inference, hierarchical modelling, and clustering. See <a rel="nofollow" href="https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf" target="_blank">Teh, Y. W. (2011)</a> and the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/dirichletprocess/vignettes/dirichletprocess.pdf" target="_blank">vignette</a> for details.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/dirichletprocess.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=detpack" target="_blank">detpack</a> v1.0.1: Enables density estimation for possibly large data sets and conditional/unconditional random number generation with distribution element trees. For details on distribution element trees, see <a rel="nofollow" href="arxiv:1610.00345" target="_blank">Meyer (2016)</a>, <a rel="nofollow" href="doi:10.1007/s11222-017-9751-9" target="_blank">Meyer (2017)</a>, and <a rel="nofollow" href="arxiv:1711.04632" target="_blank">Meyer (2017)</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/detpack.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=gnorm" target="_blank">gnorm</a> v1.0.0: Provides functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities, and random deviates. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/gnorm/vignettes/gnormUse.html" target="_blank">vignette</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=IROmiss" target="_blank">IROmiss</a> v1.0.1: Provides a general algorithm, the Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. See <a rel="nofollow" href="arxiv:1802.02251" target="_blank">Liang et al. (2018)</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=KRIG" target="_blank">KRIG</a> v0.1.0: Provides functions for Kriging models and various methods for spatial statistics, including multivariate sensitivity analysis using reproducing kernel Hilbert spaces and computation of Sobol indexes. There are vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/KRIG/vignettes/ordinary_kriging.html" target="_blank">Ordinary Kriging</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/KRIG/vignettes/simple_kriging.html" target="_blank">Simple Kriging</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/KRIG/vignettes/universal_kriging.html" target="_blank">Universal Kriging</a>, and a worked <a rel="nofollow" href="https://cran.rstudio.com/web/packages/KRIG/vignettes/copper_mining_2d.html" target="_blank">example</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/KRIG.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=natural" target="_blank">natural</a> v0.9.0: Implements two error variance estimation methods in high-dimensional linear models. See <a rel="nofollow" href="arxiv:1712.02412" target="_blank">Yu, Bien (2017)</a> and the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/natural/vignettes/using_natural.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=OpVaR" target="_blank">OpVar</a> v1.0: Provides functions for modeling operational (value-at-)risk, including loss frequencies and loss severities with plain, mixed (<a rel="nofollow" href="doi:10.1023/A:1024072610684" target="_blank">Frigessi et al. (2012)</a>) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (<a rel="nofollow" href="doi:10.21314/JOP.2013.131" target="_blank">Ergashev et al. (2013)</a>). The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/OpVaR/vignettes/OpVaR_vignette.html" target="_blank">vignette</a> shows some examples.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=netrankr" target="_blank">netrankr</a> v0.2.0: Implements methods for centrality-related analyses of networks, focusing on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. See <a rel="nofollow" href="doi:10.1016/j.socnet.2017.12.003" target="_blank">Schoch (2018)</a>. There are vignettes for <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/benchmarks.html" target="_blank">benchmarks</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/centrality_indices.html" target="_blank">centrality indices</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/indirect_relations.html" target="_blank">indirect relations</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/neighborhood_inclusion.html" target="_blank">neighborhood inclusion</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/partial_centrality.html" target="_blank">partial centrality</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/positional_dominance.html" target="_blank">positional dominance</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/probabilistic_cent.html" target="_blank">probabilistic centrality</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/threshold_graph.html" target="_blank">uniquely ranked graphs</a>, and a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/netrankr/vignettes/use_case.html" target="_blank">use case</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/netrankr.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=palmtree" target="_blank">palmtree</a> v0.9.0: Implements the PALM tree algorithm, an extension to the MOB algorithm (implemented in the <code>partykit</code> package), where some parameters are fixed across all groups. See <a rel="nofollow" href="arxiv:1612.07498" target="_blank">Seibold et al. (2016)</a> for details.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/palmtree.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.rstudio.com/web/packages/PMCMRplus/" target="_blank">PMCMRplus</a> v1.0.0: Provides functions to calculate many different types of pairwise multiple comparisons tests. See the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/PMCMRplus/vignettes/QuickReferenceGuide.html" target="_blank">vignette</a> for charts listing the tests covered.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=seminr" target="_blank">seminr</a> v0.4.0: Implements a domain-specific language for building PLS structural equation models, allowing for the latest estimation methods for Consistent PLS as per <a rel="nofollow" href="http://aisel.aisnet.org/misq/vol39/iss2/4/" target="_blank">Dijkstra &amp; Henseler (2015)</a>, adjusted interactions as per <a rel="nofollow" href="doi:10.1080/10705510903439003" target="_blank">Henseler &amp; Chin (2010)</a>, and bootstrapping utilizing parallel processing as per <a rel="nofollow" href="https://www.amazon.com/Partial-Squares-Structural-Equation-Modeling/dp/148337744X" target="_blank">Hair et al. (2017)</a>. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/seminr/vignettes/SEMinR.html" target="_blank">vignette</a>.</p>
<h2>Time Series</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=santaR" target="_blank">santaR</a> v1.0: Provides a graphical, automated pipeline for the analysis of short time series that has been designed to accommodate asynchronous time sampling, inter-individual variability, noisy measurements and large numbers of variables. There is a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/santaR/vignettes/getting-started.html" target="_blank">Getting Started Guide</a> and vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/santaR/vignettes/advanced-command-line-functions.html" target="_blank">advanced command line functions</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/santaR/vignettes/automated-command-line.html" target="_blank">automated command line functions</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/santaR/vignettes/plotting-options.html" target="_blank">plotting options</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/santaR/vignettes/prepare-input-data.html" target="_blank">preparing input</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/santaR/vignettes/selecting-optimal-df.html" target="_blank">selecting degrees of freedom</a>, the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/santaR/vignettes/theoretical-background.html" target="_blank">theoretical background</a>, and the <a rel="nofollow" href="santar:%20Graphical%20user%20interface" target="_blank">GUI</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/santaR.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=TSrepr" target="_blank">TSrepr</a> v1.0.0: Provides methods for representations (e.g., dimensionality reduction, preprocessing, feature extraction) of time series. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/TSrepr/vignettes/TSrepr_extentions.html" target="_blank">Introduction to the Framework</a>, a vignettes on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/TSrepr/vignettes/TSrepr_representations_of_time_series.html" target="_blank">representations</a>, and a <a rel="nofollow" href="https://cran.rstudio.com/web/packages/TSrepr/vignettes/TSrepr_representations_use_case.html" target="_blank">Use Case</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=TSstudio" target="_blank">TSstudio</a> v0.1.1: Provides a set of interactive visualization tools for time series analysis supporting ts, mts, zoo and xts objects including visualization functions for forecasting model performance (forecasted vs. actual), time series interactive plots (single and multiple series), and seasonality plots. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/TSstudio/vignettes/TSstudio_Intro.html" target="_blank">vignette</a> shows the features available.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/TSstudio.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h2>Utilities</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=arrangements" target="_blank">arrangements</a> v1.0.2: Provides fast generators and iterators for permutations, combinations and partitions, allowing users to generate arrangements in a memory-efficient manner. Benchmarks may be found <a rel="nofollow" href="https://randy3k.github.io/arrangements/articles/benchmark.html" target="_blank">here</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=fs" target="_blank">fs</a> v1.1.0: Implements a cross-platform interface to file system operations, built on top of the <code>libuv</code> C library. See <a rel="nofollow" href="https://cran.rstudio.com/web/packages/fs/README.html" target="_blank">README</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=googlePolylines" target="_blank">googlePolylines</a> v0.4.0: Provides functions to encode simple feature (<code>sf</code>) objects and coordinates using the <a rel="nofollow" href="https://developers.google.com/maps/documentation/utilities/polylinealgorithm" target="_blank">Google polyline encoding algorithm</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/googlePolylines/vignettes/sfencode.html" target="_blank">vignette</a> introduces the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=prrd" target="_blank">prrd</a> v0.2.0: Provides functions to queue reverse depends for a given package, such that multiple workers can run the tests in parallel. Look <a rel="nofollow" href="https://www.r-pkg.org/pkg/prrd" target="_blank">here</a> or in the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/prrd/README.html" target="_blank">README</a> for functionality details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rquery" target="_blank">rquery</a> v0.3.1: Implements a query generator based on <a rel="nofollow" href="https://en.wikipedia.org/wiki/Edgar_F._Codd" target="_blank">Edgar F. Codd’s</a> relational algebra and operator names, which is aimed at enhancing the experience using ‘SQL’ at big-data scale. There is a vignette on the <a rel="nofollow" href="https://cran.rstudio.com/web/packages/rquery/vignettes/AssigmentPartitioner.html" target="_blank">Assignment Partitioner</a> and one on <a rel="nofollow" href="https://cran.rstudio.com/web/packages/rquery/vignettes/QueryGeneration.html" target="_blank">Query Generation</a>.</p>
<p><a rel="nofollow" href="https://cran.rstudio.com/web/packages/tsibble/" target="_blank">tsibble</a> v0.1.3: Provides a <code>tbl_ts</code> class, the <code>tsibble</code>, to store and manage temporal-context data in a data-centric format. There is an <a rel="nofollow" href="https://cran.rstudio.com/web/packages/tsibble/vignettes/intro-tsibble.html" target="_blank">Introduction</a>.</p>
<h2>Visualizations</h2>
<p><a rel="nofollow" href="https://cran.r-project.org/package=breakDown" target="_blank">breakDown</a> v0.1.3: Implements break-down plots which show the contribution of every variable present in the model. Vignettes cover <a rel="nofollow" href="https://cran.rstudio.com/web/packages/breakDown/vignettes/break_lm.html" target="_blank">linear models</a>, <a rel="nofollow" href="https://cran.rstudio.com/web/packages/breakDown/vignettes/break_glm.html" target="_blank">GLMs</a>, and <a rel="nofollow" href="https://cran.rstudio.com/web/packages/breakDown/vignettes/break_ranger.html" target="_blank"><code>ranger</code> models</a>.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/breakDown.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=sigmaNet" target="_blank">sigmaNet</a> v1.0.3: Offers functions to create interactive graph visualizations using <a rel="nofollow" href="http://sigmajs.org/" target="_blank">Sigma.js</a>. The <a rel="nofollow" href="https://cran.rstudio.com/web/packages/sigmaNet/vignettes/getting-started.html" target="_blank">vignette</a> shows how to get started.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-02-18-Rickert-Jan2018-Top40_files/sigmaNet.png?w=456&ssl=1" data-recalc-dims="1"></p>
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