<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/june-2018-top-40-new-packages/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="https://rviews.rstudio.com/2018/07/29/june-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>Approximately 144 new packages stuck to CRAN in June. That fact that 31 of these are specialized to particular scientific disciplines or analyses provides some evidence to my hypothesis that working scientists are actively adopting R. Below are my Top 40 picks for June, organized into the categories of Computational Methods, Data, Data Science, Economics, Science, Statistics, Time Series, Utilities and Visualizations. The Data packages, especially <code>rtrek</code> and <code>opensensmapr</code>, look like they have some interesting new data to explore.</p>
<h3>Computational Methods</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=nnTensor" target="_blank">nnTensor</a> v0.99.1: Provides methods for n-negative matrix factorization and decomposition. See <a rel="nofollow" href="doi:10.1002/9780470747278" target="_blank">Cichock et al (2009)</a> for details.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=RcppEigenAD" target="_blank">RcppEigenAD</a> v1.0.0: Provides functions to compile <code>C++</code> code using <code>Rcpp</code>, <code>Eigen</code>, and <code>CppAD</code> to produce first- and second-order partial derivatives, and also provides an implementation of Faa’ di Bruno’s formula to combine the partial derivatives of composed functions. See <a rel="nofollow" href="arxiv:math/0601149v1" target="_blank">Hardy (2006)</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rcane" target="_blank">rcrane</a> v1.0: Provides optimization algorithms to estimate coefficients in models such as linear regression and neural networks. Includes batch gradient descent, stochastic gradient descent, minibatch gradient descent, and coordinate descent. See [Kiwiel, (2001)](doi:10.1007/PL00011414, <a rel="nofollow" href="isbn:1-4020-7553-7" target="_blank">Yu Nesterov (2004)</a>, <a rel="nofollow" href="doi:10.1080/01621459.1982.10477894" target="_blank">Ferguson (1982)</a>, <a rel="nofollow" href="arxiv:1212.5701" target="_blank">Zeiler (2012)</a>, and <a rel="nofollow" href="arxiv:1502.04759" target="_blank">Wright (2015)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/rcane/vignettes/rcane.html" target="_blank">vignette</a> introduces the package.</p>
<h3>Data</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=bjscrapeR" target="_blank">bjscrapeR</a> v0.1.0: Scrapes crime data from the <a rel="nofollow" href="https://www.bjs.gov/developer/ncvs/methodology.cfm" target="_blank">National Crime Victimization Survey</a>, which tracks personal and household crime in the USA.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=genesysr" target="_blank">genesysr</a> v0.9.1: Implements an API to access data on plant genetic resources from genebanks around the world published on <a rel="nofollow" href="https://www.genesys-pgr.org/" target="_blank">Genesys</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/genesysr/vignettes/tutorial.html" target="_blank">vignette</a> offers a short tutorial.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=opensensmapropem" target="_blank">opensensmapr</a> v0.4.1: Allows users to download real-time environmental measurements and sensor station metadata from the <a rel="nofollow" href="https://opensensemap.org/" target="_blank">OpenSenseMap</a> API. There are vignettes for <a rel="nofollow" href="https://cran.r-project.org/web/packages/opensensmapr/vignettes/osem-history.html" target="_blank">Visualization</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/opensensmapr/vignettes/osem-intro.html" target="_blank">Exploration</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/opensensmapr/vignettes/osem-serialization.html" target="_blank">Caching Data for Reproducibility</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/opensense.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=readabs" target="_blank">readabs</a> v0.2.1: Provides functions to read <code>Excel</code> files from the Australian Bureau of Statistics into Tidy Data Sets. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/readabs/vignettes/my-vignette.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rppo" target="_blank">rppo</a> v1.0: Implements an interface to the <a rel="nofollow" href="https://www.plantphenology.org/" target="_blank">Global Plant Phenology Data Portal</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/rppo/vignettes/rppo-vignette.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rtrek" target="_blank">rtrek</a> v0.1.0: Provides datasets related to the Star Trek fictional universe, functions for working with the data, and access to real-world datasets based on the televised series and other related licensed media productions. It interfaces with <a rel="nofollow" href="https://www.wikipedia.org/" target="_blank">Wikipedia</a>, the <a rel="nofollow" href="http://stapi.co/" target="_blank">Star Trek API (STAPI)</a>, <a rel="nofollow" href="http://memory-alpha.wikia.com/wiki/Portal:Main" target="_blank">Memory Alpha</a>, and <a rel="nofollow" href="http://memory-beta.wikia.com/wiki/Main_Page" target="_blank">Memory Beta</a> to retrieve data, metadata, and other information relating to Star Trek. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/rtrek/readme/README.html" target="_blank">README</a> for usage information.</p>
<p><img src="https://i0.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/rtrek.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=skynet" target="_blank">skynet</a> v1.2.2: Implements methods for generating air transport statistics based on publicly available data from the <a rel="nofollow" href="https://www.transtats.bts.gov/databases.asp?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2=0" target="_blank">U.S. Bureau of Transport Statistics (BTS)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/skynet/vignettes/skynet.html" target="_blank">vignette</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/skynet.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Data Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=AdaSampling" target="_blank">AdaSampling</a> v1.1: Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. See <a rel="nofollow" href="doi:10.1109/TCYB.2018.2816984" target="_blank">Yang et al. (2018)</a> and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/AdaSampling/vignettes/vignette.html" target="_blank">vignette</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=AROC" target="_blank">AROC</a> v1.0: Provides functions to estimate the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve. See <a rel="nofollow" href="arxiv:1806.00473" target="_blank">de Carvalho and Rodriguez-Alvarez (2018)</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/AROC.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=cloudml" target="_blank">cloudml</a> v0.5.1: Provides an interface to the Google Cloud Machine Learning Platform. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/cloudml/vignettes/getting_started.html" target="_blank">Getting Sarted Guide</a> and vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/cloudml/vignettes/deployment.html" target="_blank">Deploying Models</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/cloudml/vignettes/storage.html" target="_blank">Cloud storage</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/cloudml/vignettes/training.html" target="_blank">Training</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/cloudml/vignettes/tuning.html" target="_blank">Hyperparameter Tuning</a>.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/cloudml.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=reclin" target="_blank">reclin</a> v0.1.0: Provide functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities, forcing one-to-one matching. There is an <a rel="nofollow" href="https://rviews.rstudio.com/2018/07/29/june-2018-top-40-new-packages/Introduction%20to%20reclin" target="_blank">Introduction</a> and a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/reclin/vignettes/deduplication.html" target="_blank">Duplication</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vip" target="_blank">vip</a> v0.1.0: Provides a general framework for constructing variable importance plots from various types of machine learning models, based on a novel approach using partial dependence plots and individual conditional expectation curves as described in <a rel="nofollow" href="arxiv:1805.04755" target="_blank">Greenwell et al. (2018)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/vip/readme/README.html" target="_blank">README</a> for details and examples.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/vip.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=wevid" target="_blank">wevid</a> v0.4.2: Provides functions to quantify the performance of a binary classifier through weight of evidence. These can be used with any test dataset on which you have observed case-control status, and have computed prior and posterior probabilities of case status using a model learned on a training dataset. Look at this <a rel="nofollow" href="http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/wevidtutorial.html" target="_blank">website</a> for details and examples.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/wevid.png?w=456&ssl=1" data-recalc-dims="1"></p>
<h3>Economics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=trade" target="_blank">trade</a> v0.5.3: Provides tools for working with trade model, including the ability to calibrate different consumer-demand systems and simulate the effects of tariffs and quotas under different competitive regimes. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/trade/vignettes/Reference.html" target="_blank">vignette</a> provides details.</p>
<h3>Science</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=linpk" target="_blank">linpk</a> v1.0: Provides functions and a shiny application to generate concentration-time profiles from linear pharmacokinetic (PK) systems. Single or multiple doses may be specified. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/linpk/vignettes/linpk-intro.html" target="_blank">vignette</a> offers details and examples.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/linpk.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ratematrix" target="_blank">ratematrix</a> v1.0: Provides functions to estimate the evolutionary rate matrix ® using Markov chain Monte Carlo (MCMC), as described in <a rel="nofollow" href="doi:10.1111/2041-210X.12826" target="_blank">Caetano and Harmon (2017)</a>. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/ratematrix/vignettes/Set_custom_starting_point.html" target="_blank">Setting a custom starting point</a> and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/ratematrix/vignettes/Making_prior_on_ratematrix.html" target="_blank">Using prior distributions</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/ratematrix.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=spectralAnalysis" target="_blank">spectralAnalysis</a> v3.12.0: Provides a toolkit for spectral-analysis, enabling users to pre-process, visualize, and analyse process analytical dat, by spectral data measurements made during a chemical process.</p>
<h3>Statistics</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=betaboost" target="_blank">betaboost</a> v1.0.1: Implements boosting beta regression for potentially high-dimensional data <a rel="nofollow" href="doi:10.1093/ije/dyy093" target="_blank">Mayr et al. (2018)</a> using the same parametrization as <code>betareg</code> <a rel="nofollow" href="doi:10.18637/jss.v034.i02" target="_blank">Cribari-Neto and Zeileis (2010)</a>. The underlying algorithms are implemented via the R add-on packages <code>mboost</code> <a rel="nofollow" href="doi:10.1007/s00180-012-0382-5" target="_blank">Hofner et al. (2014)</a> and <code>gamboostLSS</code> <a rel="nofollow" href="doi:10.1111/j.1467-9876.2011.01033.x" target="_blank">Mayr et al. (2012)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/betaboost/vignettes/Using_betaboost_IJE.html" target="_blank">vignette</a> offers examples.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=bfw" target="_blank">bfw</a> v0.1.0: Provides a framework for conducting Bayesian analysis using Markov chain Monte Carlo with the <a rel="nofollow" href="http://mcmc-jags.sourceforge.net/" target="_blank">JAGS</a> sampler. There are vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/bfw/vignettes/fit_latent_data.html" target="_blank">Fitting Latent Data</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/bfw/vignettes/fit_observed_data.html" target="_blank">Fitting Observed Data</a>, the <a rel="nofollow" href="https://cran.r-project.org/web/packages/bfw/vignettes/metric.html" target="_blank">Predict Metric</a>, <a rel="nofollow" href="https://cran.r-p%5Broject.org/web/packages/bfw/vignettes/plot_data.html" target="_blank">Plotting</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/bfw/vignettes/regression.html" target="_blank">Regression</a>.</p>
<p><img src="https://i1.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/bfw.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=CaseBasedReasoning" target="_blank">CaseBasedReasoning</a> v0.1: Given a large set of problems and their individual solutions, case-based reasoning seeks to solve a new problem by referring to the solution of that problem that is “most similar” to the new problem. See <a rel="nofollow" href="doi:10.1016/S0167-9473(02)00058-0" target="_blank">Dippon et al. (2002)</a>, the vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/CaseBasedReasoning/vignettes/Distance_Measures.html" target="_blank">Motivation</a>, and examples of case-based reasoning with a <a rel="nofollow" href="https://cran.r-project.org/web/packages/CaseBasedReasoning/vignettes/Cox-Beta-Model.html" target="_blank">Cox-Beta Model</a> and a <a rel="nofollow" href="https://cran.r-project.org/web/packages/CaseBasedReasoning/vignettes/RandomForest-Model.html" target="_blank">Random Forest Model</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/case.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=coxed" target="_blank">coxed</a> v0.1.1: Provides functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model. There is a vignette on using the <a rel="nofollow" href="https://cran.r-project.org/web/packages/coxed/vignettes/coxed.html" target="_blank">coxed() function</a> and another on <a rel="nofollow" href="https://cran.r-project.org/web/packages/coxed/vignettes/simulating_survival_data.html" target="_blank">simulating survival data</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/coxed.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=GLMMadaptive" target="_blank">GLMMadaptive</a> v0.2-0: Provides functions to fit generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule. See <a rel="nofollow" href="doi:10.1080/10618600.1995.10474663" target="_blank">Pinheiro and Bates (1995)</a> and the vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/Custom_Models.html" target="_blank">Custom Models</a>,<br>
<a rel="nofollow" href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/GLMMadaptive_basics.html" target="_blank">GLMMadaptive Basics</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/Methods_MixMod.html" target="_blank">Methods for MixMod Objects</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/ZeroInflated_and_TwoPart_Models.html" target="_blank">Zero-Inflated and Two-Part Mixed Effects Models</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=glmmfields" target="_blank">glmmfields</a> v0.1.0: Implements generalized linear mixed models with robust random fields for spatiotemporal modeling. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/glmmfields/vignettes/spatial-glms.html" target="_blank">vignette</a> provides examples.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/glmmfields.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=kendallRandomWalks" target="_blank">kendallRandomWalks</a> v0.9.3: Provides functions for simulating Kendall random walks, continuous-space Markov chains generated by the Kendall generalized convolution. See <a rel="nofollow" href="arxiv:1412.0220" target="_blank">Jasiulis-Gołdyn (2014)</a> for details and the vignettes <a rel="nofollow" href="https://cran.r-project.org/web/packages/kendallRandomWalks/vignettes/kendall_rws.html" target="_blank">Kendall Random Walks</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/kendallRandomWalks/vignettes/behaviour.html" target="_blank">Studying the Behavior of Kendall Random Walks</a>.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/kendall.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=netSEM" target="_blank">netSEM</a> v0.5.0: Provides functions for structural equation modeling. There is an <a rel="nofollow" href="https://cran.r-project.org/web/packages/netSEM/vignettes/netSEM.html" target="_blank">Introduction</a> and vignettes on <a rel="nofollow" href="https://cran.r-project.org/web/packages/netSEM/vignettes/Backsheet.html" target="_blank">Backsheet Degradation</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/netSEM/vignettes/Crack.html" target="_blank">Backsheet Cracking</a>, <a rel="nofollow" href="https://cran.r-project.org/web/packages/netSEM/vignettes/IVfeature.html" target="_blank">Current Voltage Features</a>, and <a rel="nofollow" href="https://cran.r-project.org/web/packages/netSEM/vignettes/pet.html" target="_blank">Modeling of the Weathering Driven Degradation of Poly(ethylene-terephthalate) Films</a>.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/netSEM.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=umap" target="_blank">umap</a> v0.1.0.3: Implements the uniform manifold approximation and projection technique for dimension reduction as described in <a rel="nofollow" href="arxiv:1802.03426" target="_blank">McInnes and Healy (2018)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/umap/vignettes/umap.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vimp" target="_blank">vimp</a> v1.0.0:Provides functions to calculate point estimates of, and valid confidence intervals for, non-parametric variable importance measures in high and low dimensions. For information about the methods, see <a rel="nofollow" href="https://biostats.bepress.com/uwbiostat/paper422/" target="_blank">Williamson et al. (2017)</a>. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/vimp/vignettes/introduction_to_vimp.html" target="_blank">vignette</a> contains an introduction to the package.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/vimp.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=vsgoftest" target="_blank">vsgoftest</a> v0.3-2: Implements Vasicek and Song goodness-of-fit tests (based on Kullbach-Leibler divergence) for a family of distributions that include uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace, and beta distributions. See <a rel="nofollow" href="arxiv:1806.07244" target="_blank">Lequesne and Regnault (2018)</a> for details and the <a rel="nofollow" href="https://cran.r-project.org/web/packages/vsgoftest/vignettes/vsgoftest_tutorial.pdf" target="_blank">Tutorial</a>.</p>
<h3>Time Series</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=anomaly" target="_blank">anomaly</a> v1.0.0: Implements the CAPA (Collective And Point Anomaly) algorithm of <a rel="nofollow" href="arxiv:1806.01947" target="_blank">Fisch, Eckley and Fearnhead (2018)</a> for the detection of anomalies in time series data.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=exuber" target="_blank">exuber</a> v0.1.0: Provides functions for testing and dating periods of explosive dynamics (exuberance) in time series using recursive unit root tests as proposed by <a rel="nofollow" href="doi:10.1111/iere.12132" target="_blank">Phillips et al. (2015)</a>. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/exuber/readme/README.html" target="_blank">README</a> to get started.</p>
<p>Simulate a variety of periodically-collapsing bubble models. The estimation and simulation utilizes the matrix inversion lemma from the recursive least squares algorithm, which results in a significant speed improvement.</p>
<h3>Utilities</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=BiocManager" target="_blank">BiocManager</a> v1.30.1: Implements a tool to install and update Bioconductor packages. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/BiocManager/vignettes/BiocManager.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=IntervalSurgeon" target="_blank">IntervalSurgeon</a> v1.0: Provides functions for manipulating integer-bounded intervals including finding overlaps, piling, and merging. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/IntervalSurgeon/vignettes/intro.html" target="_blank">vignette</a> shows how to use the package.</p>
<p><img alt="" src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/interval.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=pkgbuild" target="_blank">pkgbuild</a> v1.0.0: Provides functions used to build R packages. Locates compilers needed to build R packages on various platforms and ensures the PATH is configured appropriately.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=rqdatatable" target="_blank">rqdatatable</a> v0.1.2: Implements the <code>rquery</code> piped query algebra using <code>data.table</code>. There is a vignette on <a rel="nofollow" href="https://cran.r-project.org/web/packages/rqdatatable/vignettes/GroupedSampling.html" target="_blank">Grouped Sampling</a> and a <a rel="nofollow" href="https://cran.r-project.org/web/packages/rqdatatable/vignettes/logisticexample.html" target="_blank">Logistic Example</a>.</p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=ssh" target="_blank">ssh</a> v0.2: Provides functions to connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client. There is a <a rel="nofollow" href="https://cran.r-project.org/web/packages/ssh/vignettes/intro.html" target="_blank">vignette</a>.</p>
<h3>Visualization</h3>
<p><a rel="nofollow" href="https://cran.r-project.org/package=mgcViz" target="_blank">mgcViz</a> v0.1.1: An extension of the <code>mgcv</code> package, providing visual tools for Generalized Additive Models (GAMs) that exploit the additive structure of GAMs, scale to large data sets, and can be used in conjunction with a wide range of response distributions. See the <a rel="nofollow" href="https://cran.r-project.org/web/packages/mgcViz/vignettes/mgcviz.html" target="_blank">vignette</a> for examples.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/mgcViz.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p><a rel="nofollow" href="https://cran.r-project.org/package=tiler" target="_blank">tiler</a> v0.2.0: Provides functions to create geographic map tiles from geospatial map files or non-geographic map tiles from simple image files. The <a rel="nofollow" href="https://cran.r-project.org/web/packages/tiler/vignettes/tiler.html" target="_blank">vignette</a> provides an introduction.</p>
<p><img src="https://i2.wp.com/rviews.rstudio.com/post/2018-07-21-June-Top40_files/tiler.png?w=450&ssl=1" data-recalc-dims="1" height="500"></p>
<p> </p>
	<p>
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