R packages on CRAN

NameLatest ReleaseDownloads1
  1. Downloads: total number of downloads from the Rstudio CRAN mirror since October 2012. 

{abclass}: Angle-Based Large-Margin Classifiers

gpl-badge abclass-dev abclass-ghs

{abclass} provides implementations of the multi-category angle-based classifiers (Zhang & Liu, 2014) with the large-margin unified machines (Liu, et al., 2011) for high-dimensional data.


  • Zhang, C., & Liu, Y. (2014). Multicategory Angle-Based Large-Margin Classification. Biometrika, 101(3), 625–640.
  • Liu, Y., Zhang, H. H., & Wu, Y. (2011). Hard or soft classification? large-margin unified machines. Journal of the American Statistical Association, 106(493), 166–177.

{clusrank}: Wilcoxon Rank Tests for Clustered Data

gpl-badge clusrank-dev clusrank-ghs

{clusrank} provides functions for Wilcoxon rank sum test and Wilcoxon signed rank test for clustered data. See Jiang et. al (2020) for details.


  • Jiang, Y., He, X., Lee, M. T., Rosner, B., & Yan, J. (2020). Wilcoxon rank-based tests for clustered data with R package clusrank. Journal of Statistical Software, 96(6), 1–26.

{dynsurv}: Dynamic Models for Survival Data

gpl-badge dynsurv-dev dynsurv-ghs

{dynsurv} provides functions to fit time-varying coefficient models for interval censored and right censored survival data. Three major approaches are implemented:

  1. Bayesian Cox model with time-independent, time-varying or dynamic coefficients for right censored and interval censored data
  2. Spline based time-varying coefficient Cox model for right censored data
  3. Transformation model with time-varying coefficients for right censored data using estimating equations.


  • Wang, X., Chen, M., & Yan, J. (2013). Bayesian dynamic regression models for interval censored survival data with application to children dental health. Lifetime Data Analysis, 19(3), 297–316.
  • Wang, W., Chen, M., Chiou, S. H., Lai, H., Wang, X., Yan, J., & Zhang, Z. (2016). Onset of persistent pseudomonas aeruginosa infection in children with cystic fibrosis with interval censored data. BMC Medical Research Methodology, 16(1), 122.

{formatBibtex}: Format BibTeX Entries and Files

gpl-badge formatBibtex-dev formatBibtex-ghs

{formatBibtex} provides utility tools to format BibTeX entries and files in an opinionated way.

{intsurv}: Integrative Survival Modeling

gpl-badge intsurv-dev intsurv-ghs

{intsurv} contains implementations of

  • integrative Cox model with uncertain event times (Wang et al., 2020)
  • Cox cure rate model with uncertain event status (Wang et al., 2020)

and other survival analysis routines, including

  • regular Cox cure rate model
  • regularized Cox cure rate model with elastic net penalty
  • weighted concordance index


  • Wang, W., Aseltine, R. H., Chen, K., & Yan, J. (2020). Integrative Survival Analysis with Uncertain Event Times in Application to A Suicide Risk Study. Annals of Applied Statistics, 14(1), 51-73.
  • Wang, W., Luo, C., Aseltine, R. H., Wang, F., Yan, J., & Chen, K. (2020). Suicide Risk Modeling with Uncertain Diagnostic Records. arXiv preprint arXiv:2009.02597.

{jds.rmd}: R Markdown Templates for Journal of Data Science

gpl-badge jds.rmd-dev jds.rmd-ghs

{jds.rmd} provides R Markdown templates intended for Journal of Data Science, which can be useful for authoring a manuscript with code chunks or producing tables/figures on the fly.

{reda}: Recurrent Event Data Analysis

gpl-badge reda-dev reda-ghs

{reda} provides functions for

  1. simulating survival, recurrent event, and multiple event data from stochastic process point of view
  2. exploring and modeling recurrent event data through the mean cumulative function (MCF) or also called the Nelson-Aalen estimator of the cumulative hazard rate function, and gamma frailty model with spline rate function
  3. comparing two-sample recurrent event responses with the pseudo-score tests

{rrpack}: Reduced-Rank Regression


{rrpack} provides implementations for multivariate regression methodologies including reduced-rank regression (RRR), reduced-rank ridge regression (RRS), robust reduced-rank regression (R4), generalized/mixed-response reduced-rank regression (mRRR), row-sparse reduced-rank regression (SRRR), reduced-rank regression with a sparse singular value decomposition (RSSVD), and sparse and orthogonal factor regression (SOFAR).

{splines2}: Regression Spline Functions and Classes

gpl-badge splines2-dev splines2-ghs JDS

{splines2} is a supplement package to the base package {splines}. It provides functions to construct basis matrices of

  • B-splines
  • M-splines
  • I-splines
  • convex splines (C-splines)
  • periodic splines
  • natural cubic splines
  • generalized Bernstein polynomials
  • their integrals (except C-splines) and derivatives of given order by closed-form recursive formulas

In addition to the R interface, {splines2} provides a C++ header-only library integrated with {Rcpp}, which allows the construction of spline basis functions directly in C++ with the help of {Rcpp} and {RcppArmadillo}. Thus, it can also be treated as one of the Rcpp* packages. See Wang and Yan (2021) for details.


  • Wang, W., & Yan, J. (2021). Shape-restricted regression splines with R package splines2. Journal of Data Science, 19(3), 498–517.

{touch}: Tools of Utilization and Cost in Healthcare

gpl-badge touch-dev touch-ghs

{touch} provides R implementation of the software tools developed in the H-CUP (Healthcare Cost and Utilization Project) and AHRQ (Agency for Healthcare Research and Quality). It contains functions for

  1. mapping ICD-9 codes to the AHRQ comorbidity measures
  2. translating ICD-9 (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General Equivalence Mappings) from CMS (Centers for Medicare and Medicaid Services)

Python Tools

touchpy: Tools of Utilization and Cost in Healthcare in Python

gpl-badge touchpy-dev touchpy-ghs

touchpy is intended to be a Python version of {touch} and provides functionalities to map ICD-9 or ICD-10 code to AHRQ comorbidity measures.