This package provides functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) <doi:10.1056/NEJMc1808567>. Please see the vignette for a quickstart guide.

This package provides tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .

This package provides tools for creating, manipulating and reading Research Object Crates (RO-Crates), a lightweight approach to packaging research data with structured metadata. Includes utilities for metadata generation, entity management, validation and reading existing RO-Crates following the specification <https://w3id.org/ro/crate/1.2/>.

The routine twosample_test() in this package runs the two sample test using various test statistic. The p values are found via permutation or large sample theory. The routine twosample_power() allows the calculation of the power in various cases, and plot_power() draws the corresponding power graphs. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones.

This package provides R-squared values and standardized regression coefficients for linear models applied to multiply imputed datasets as obtained by mice'. Confidence intervals, zero-order correlations, and alternative adjusted R-squared estimates are also available. The methods are described in Van Ginkel and Karch (2024) <doi:10.1111/bmsp.12344> and in Van Ginkel (2020) <doi:10.1007/s11336-020-09696-4>.

Earth Engine <https://earthengine.google.com/> client library for R. All of the Earth Engine API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See <https://r-spatial.github.io/rgee/> for further details.

Easily download datasets from Kaggle <https://www.kaggle.com/> directly into your R environment using RKaggle'. Streamline your data analysis workflows by importing datasets effortlessly and focusing on insights rather than manual data handling. Perfect for data enthusiasts and professionals looking to integrate Kaggle datasets into their R projects with minimal hassle.

Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data.

Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators.

Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.

Packed bar charts are a variation of treemaps for visualizing skewed data. The concept was introduced by Xan Gregg at JMP'.

This package performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized clinical trials. Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>. Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>. Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>. Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.1093/biomet/asaf029>. Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) <doi:10.48550/arXiv.2408.12541>.

Simplified scenario testing and sensitivity analysis, redesigned to use packages future and furrr'. Provides functions for generating function argument sets using one-factor-at-a-time (OFAT) and (sampled) permutations.

REDUCE is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in LaTeX format. REDUCE is based on Lisp and is available on the two dialects Portable Standard Lisp ('PSL') and Codemist Standard Lisp ('CSL'). The redcas package provides an interface for executing arbitrary REDUCE code interactively from R', returning output as character vectors. R code and REDUCE code can be interspersed. It also provides a specialized function for calling the REDUCE feature for solving systems of equations, returning the output as an R object designed for the purpose. A further specialized function uses REDUCE features to generate LaTeX output and post-processes this for direct use in LaTeX documents, e.g. using Sweave'.

Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the univariate part are explained in Demirtas (2017) <doi: 10.1080/03610918.2014.968725>, and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012) <doi: 10.1002/asmb.901>.

This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.

Image data used as examples in the loon R package.

Indirect method for the estimation of reference intervals (RIs) using Real-World Data ('RWD') and methods for comparing and verifying RIs. Estimates RIs by applying advanced statistical methods to routine diagnostic test measurements, which include both pathological and non-pathological samples, to model the distribution of non-pathological samples. This distribution is then used to derive reference intervals and support RI verification, i.e., deciding if a specific RI is suitable for the local population. The package also provides functions for printing and plotting algorithm results. See ?refineR for a detailed description of features. Version 1.0 of the algorithm is described in Ammer et al. (2021) <doi:10.1038/s41598-021-95301-2>. Additional guidance is in Ammer et al. (2023) <doi:10.1093/jalm/jfac101>. The verification method is described in Beck et al. (2025) <doi:10.1515/cclm-2025-0728>.

Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.

This package implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues PROCEED', DEFER', or ABSTAIN signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers.

Read Statistical Data and Metadata Exchange (SDMX) XML data. This the main transmission format used in official statistics. Data can be imported from local SDMX-ML files or a SDMX web-service and will be read in as is into a dataframe object. The RapidXML C++ library <https://rapidxml.sourceforge.net/> is used to parse the XML data.

The RDieHarder package provides an R interface to the DieHarder suite of random number generators and tests that was developed by Robert G. Brown and David Bauer, extending earlier work by George Marsaglia and others. The DieHarder library code is included.

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

This package provides statistical tools for evaluating how covariates influence the strength of Pearson correlation coefficients between two response variables. Supports bivariate normal and bivariate binary responses, with likelihood-based inference and bootstrap-based significance testing. The methodology is based on Dufera, Liu and Xu (2023) "Regression models of Pearson correlation coefficient" <doi:10.1080/24754269.2023.2164970>.