Parametric survival regression models under the maximum likelihood approach via Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.

Allow sharing sensitive information, for example passwords, API keys, etc., in R packages, using public key cryptography.

This package implements the Savvy Parity Regression savvyPR methodology for multivariate linear regression analysis. The package solves an optimization problem that balances the contribution of each predictor variable to ensure estimation stability in the presence of multicollinearity. It supports two distinct parameterization methods, a Budget-based approach that allocates a fixed loss contribution to each predictor, and a Target-based approach (t-tuning) that utilizes a relative elasticity weight for the response variable. The package provides comprehensive tools for model estimation, risk distribution analysis, and parameter tuning via cross-validation (PR1, PR2, and PR3 model types) to optimize predictive accuracy. Methods are based on Asimit, Chen, Ichim and Millossovich (2026) <https://openaccess.city.ac.uk/id/eprint/37017/>.

Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the implemented methods.

This package provides tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

This package provides a function sfc() to compute the substance flow with the input files --- "data" and "model". If sample.size is set more than 1, uncertainty analysis will be executed while the distributions and parameters are supplied in the file "data".

This package provides a tool for simulating rhythmic data: transcriptome data using Gaussian or negative binomial distributions, and behavioral activity data using Bernoulli or Poisson distributions. See Singer et al. (2019) <doi:10.7717/peerj.6985>.

Stores objects (e.g. neural networks) that are needed for using Sojourn accelerometer methods. For more information, see Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

Identifies individuals in a social network who should be the intervention subjects for a network intervention in which you have a group of targets, a group of avoiders, and a group that is neither.

The functions allow for the numerical evaluation of some commonly used entropy measures, such as Shannon entropy, Rényi entropy, Havrda and Charvat entropy, and Arimoto entropy, at selected parametric values from several well-known and widely used probability distributions. Moreover, the functions also compute the relative loss of these entropies using the truncated distributions. Related works include: Awad, A. M., & Alawneh, A. J. (1987). Application of entropy to a life-time model. IMA Journal of Mathematical Control and Information, 4(2), 143-148. <doi:10.1093/imamci/4.2.143>.

This package provides a system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a â publication readyâ format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.

Streamline searching, downloading and formatting of nature media files (e.g. audios, photos) from online repositories. The package offers functions for obtaining media metadata from online repositories, downloading associated media files and updating data sets with new records.

Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

Used to construct the URLs and parameters of Socrata Open Data API <https://dev.socrata.com> calls, using the API's SoQL parameter format. Has method-chained and sensical syntax. Plays well with pipes.

This package implements snake in R as a programming example, see <https://en.wikipedia.org/wiki/Snake_(video_game_genre)>.

Example clinical trial data sets formatted for easy use in R.

Allows the user to connect with IBGE's (Instituto Brasileiro de Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information) SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de Recuperacao Automatica" and is the system where IBGE turns available aggregate data from their researches.

Data obtained from surveys contains information not only about the survey responses, but also the survey metadata, e.g. the original survey questions and the answer options. The surveydata package makes it easy to keep track of this metadata, and to easily extract columns with specific questions.

This package provides a set of user interface components to create outstanding shiny apps <https://shiny.posit.co/>, with the power of React JavaScript <https://react.dev/>. Seamlessly support dark and light themes, customize CSS with tailwind <https://tailwindcss.com/>.

This package provides an R interface to SymEngine <https://github.com/symengine/>, a standalone C++ library for fast symbolic manipulation. The package has functionalities for symbolic computation like calculating exact mathematical expressions, solving systems of linear equations and code generation.

Settings and functions to extend the knitr SAS engine.

Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.

It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.