March 2019: “Top 40” New CRAN Packages

April 25, 2019

(This article was first published on R Views, and kindly contributed to R-bloggers)

By my count, two hundred and thirty-three packages stuck to CRAN last month. I have tried to capture something of the diversity of the offerings by selecting packages in ten categories: Computational Methods, Data, Machine Learning, Medicine, Science, Shiny, Statistics, Time Series, Utilities, and Visualization. The Shiny category contains packages that expand on Shiny capabilities, not just packages that implement a Shiny application. It is not clear whether this is going to be a new cottage industry or not.

Computational Methods

DistributionOptimization v1.2.1: Fits Gaussian mixtures by applying Genetic algorithms from the GA package using Gaussian Mixture Logic stems from AdaptGauss.

latte v0.2.1: Implements connections to LattE for counting lattice points and integration inside convex polytopes, and 4ti2 for algebraic, geometric, and combinatorial problems on linear spaces and front-end tools facilitating their use in the ‘R’ ecosystem. Look here for an example.

nlrx v0.2.0: Provides tools to set up, run, and analyze NetLogo model simulations in R. There is a Getting Started Guide, vignettes for Advanced Configuration, and Examples.

nvctr v0.1.1: Implements the n-vector approach to calculating geographical positions using an ellipsoidal model of the Earth. This package is a translation of the FFi Matlab library from FFI described in Gade (2010). The vignette provides examples.


EHRtemporalVariability v1.0: Provides functions to delineate reference changes over time in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches, and explore results through data temporal heat maps, information geometric temporal (IGT) plots, and a Shiny app. The vignette shows how to use the package.

kayadata v0.4.0: Provides data for Kaya identity variables (population, gross domestic product, primary energy consumption, and energy-related CO2 emissions), and includes utility functions for exploring and plotting fuel mix for a given country or region. See the vignette for examples.

newsanchor v0.1.0: Implements an interface to gather news from the News API. A personal API key is required. The vignette shows how to scrape New York Times online articles.

raustats v0.1.0: Provides functions for downloading Australian economic statistics from the Australian Bureau of Statistics and Reserve Bank of Australia websites. The vignette shows how to use the package.

Machine Learning

akmedoids v0.1.2: Advances a set of R-functions for longitudinal clustering of long-term trajectories, and determines the optimal solution based on the Caliński-Harabasz criterion ( Caliński and Harabasz (1974) ). The vignette works through an extended example.

shapper v0.1.0: Implements a wrapper for the Python shap library that provides SHapley Additive exPlanations (SHAP) for the variables that influence particular observations in machine learning models. There are vignettes for classification and regression.

sparkxgb v0.1.0: Implements a sparklyr extension that provides an interface for XGBoost on Apache Spark. See the README for a brief overview.

xgb2sql v0.1.2: Enables in-database scoring of XGBoost models built in R, by translating trained model objects into SQL query. See Chen & Guestrin (2016) for details on XGBoost, and the vignette for an overview of the package.


ctrdata v0.18: Provides functions for querying, retrieving, and analyzing protocol- and results-related information on clinical trials from two public registers, the European Union Clinical Trials Register and There is a Getting Started Guide and a vignette with examples.

pubtatordb v0.1.3: Provides functions to download PubTator (National Center for Biotechnology Information) annotations, and then create and query a local version of the database. There is a vignette.

tacmagic v0.2.1: Provides functions to facilitate the analysis of positron emission tomography (PET) time activity curve (TAC) data. See Logan et al. (1996) and Aizenstein et al. (2008) for use cases, and the vignette for a detailed overview.


bulletcp v1.0.0: Provides functions to automatically detect groove locations via a Bayesian changepoint detection method, to be used in the data pre-processing step of forensic bullet matching algorithms. See Stephens (1994) for reference, the vignette for the theory, and Mejia et al. in the most recent issue of Significance for the big picture.

earthtide v0.0.5: Ports the Fortran ETERNA 3.4 program by H.G. Wenzel for calculating synthetic Earth tides using the Hartmann and Wenzel (1994) or Kudryavtsev (2004) tidal catalogs. See the vignette for an introduction.

steps v0.2.1: Implements functions to simulate population dynamics across space and time. The Eastern Grey Kangeroo vignette offers an extended example.


periscope v0.4.1: Implements an enterprise-targeted, scalable and UI-standardized shiny framework. There are vignettes for a downloadFile module, downloadablePlot module, downloadableTable module, and the creation of a framework-based application.

reactlog v1.0.0: Provides visual insight into that black box of shiny reactivity by constructing a directed dependency graph of the application’s reactive state at any point in a reactive recording. See the vignette for an introduction.

reactlog highlight filter from Carson Sievert on Vimeo.

shinyhttr v1.0.0: Modifies the progress() function from the httr package to let it send output to progressBar() function from the shinyWidgets package.


CoopGame v0.2.1: Provides a comprehensive set of tools for cooperative game theory with transferable utility, enabling users to create special families of cooperative games, such as bankruptcy games, cost-sharing games, and weighted-voting games. The vignette offers theory and examples.

discfrail v0.1: Provides functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete non-parametric distribution. See Gasperoni et. al (2018). The vignette shows the math.

fastglm v0.1.1: Provides functions to fit generalized linear models efficiently using RcppEigen. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011). There is a vignette.

hettx v0.1.1: Implements methods developed by Ding, Feller, and Miratrix (2016), and Ding, Feller, and Miratrix (2018) for testing whether there is unexplained variation in treatment effects across observations, and for characterizing the extent of the explained and unexplained variation in treatment effects. There are vignettes on heterogeneous treatment effects and systematic fariation estimation.

mcmcabn v0.1: Implements a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) and the Markov blanket resampling from Su and Borsuk (2016), and three priors: a prior controlling for structure complexity from Koivisto and Sood (2004), an uninformative prior, and a user-defined prior. The vignette provides an overview of the package.

networkABC v0.5-3: Implements a new multi-level approximation Bayesian computation (ABC) algorithm to decipher network data and assess the strength of the inferred links between network’s actors. The vignette provides an example.

retrodesign v0.1.0: Provides tools for working with Type S (Sign) and Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000) and Gelman & Carlin (2014), using the closed forms solutions for the probability of a Type S/M error from Lu, Qiu, and Deng (2018). The vignette shows how to use Type S and M errors in hypothesis testing.

senssobol v0.1.1: Enables users to compute, bootstrap, and plot up to third-order Sobol indices using the estimators by Saltelli et al. (2010) and Jansen (1999), and calculate the approximation error in the computation of Sobol first and total indices using the algorithm of Khorashadi Zadeh et al. (2017). The vignette provides an overview.

Time Series

DTSg v:0.1.2: Provides a class for working with time series data based on data.table and R6 with reference semantics. There are vignettes for Basic and Advanced usage.

RJDemetra v0.1.2: Implements an interface to JDemetra+, the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.

runstats v1.0.1: Provides methods for quickly computing time series sample statistics, including: (1) mean, (2) standard deviation, and (3) variance over a fixed-length window of time-series, (4) correlation, (5) covariance, and (6) Euclidean distance (L2 norm) between short-time pattern and time-series. See the vignette for examples.


aweek v0.2.0: Converts dates to arbitrary week definitions. The vignette provides examples.

credentials v1.1: Provides tools for managing SSH and git credentials. See the vignette for details.

cyphr v1.0.1: Implements wrappers using low-level support from sodium and OpenSSL to facilitate using encryption for data analysis. There is an Introduction and a vignette on Data Encryption.

encryptr v0.1.2: Provides functions to encrypt data frame or tibble columns using strong RSA encryption. See README for examples.

lenses v0.0.3: Provides tools for creating and using lenses to simplify data manipulation. Lenses are composable getter/setter pairs for working with data in a purely functional way, which were inspired by the Haskell library lens ( Kmett (2012) ). For a comprehensive history of lenses, see the lens wiki and look here for examples.

yum v0.0.1: Provides functions to facilitate extracting information in YAML fragments from one or multiple files, optionally structuring the information in a data.tree. See the README file.


ggasym v0.1.1: Provides functions for asymmetric matrix plotting with ggplot2. See the vignette for examples.

predict3d v:0.1.0: Provides functions for 2- and 3-dimensional plots for multiple regression models using packages ggplot2 and rgl. It supports linear models (lm), generalized linear models (glm), and local polynomial regression fittings (loess). There is a vignette.

To leave a comment for the author, please follow the link and comment on their blog: R Views. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)