Manipulate and view coronavirus data and other societally relevant data at a basic level.

Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.

Robust regression methods for compositional data. The distribution of the estimates can be approximated with various bootstrap methods. These bootstrap methods are available for the compositional as well as for standard robust regression estimates. This allows for direct comparison between them.

Estimation of sparse nonlinear functions in nonparametric regression using component selection and smoothing. Designed for the analysis of high-dimensional data, the models support various data types, including exponential family models and Cox proportional hazards models. The methodology is based on Lin and Zhang (2006) <doi:10.1214/009053606000000722>.

Compare color palettes with simulations of color vision deficiencies - deuteranopia, protanopia, and tritanopia. It includes calculation of distances between colors, and creating summaries of differences between a color palette and simulations of color vision deficiencies. This work was inspired by the blog post at <https://www.datawrapper.de/blog/colorblind-check>.

Correcting area under ROC (AUC) for measurement error based on probit-shift model.

The cmgnd implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the cmgnd reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025) <doi:10.1007/s00180-025-01638-x> and Duttilo et al (2025) <doi:10.48550/arXiv.2506.03285>.

This package provides a comprehensive and automated workflow for managing multicollinearity in data frames with numeric and/or categorical variables. The package integrates five robust methods into a single function: (1) target encoding of categorical variables based on response values (Micci-Barreca, 2001 (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); (2) automated feature prioritization to preserve key predictors during filtering; (3 and 4) pairwise correlation and VIF filtering across all variable types (numericâ numeric, numericâ categorical, and categoricalâ categorical); (5) adaptive correlation and VIF thresholds. Together, these methods enable a reliable multicollinearity management in most use cases while maintaining model integrity. The package also supports parallel processing and progress tracking via the packages future and progressr', and provides seamless integration with the tidymodels ecosystem through a dedicated recipe step.

This package contains greedy algorithms for coarse approximation linear functions.

Differential analyses and Enrichment pipeline for bulk ATAC-seq data analyses. This package combines different packages to have an ultimate package for both data analyses and visualization of ATAC-seq data. Methods are described in Karakaslar et al. (2021) <doi:10.1101/2021.03.05.434143>.

An automated and streamlined workflow for predictive climate mapping using climate station data. Works within an environment the user provides a destined path to - otherwise it's tempdir(). Quick and relatively easy creation of resilient and reproducible climate models, predictions and climate maps, shortening the usually long and complicated work of predictive modelling. For more information, please find the provided URL. Many methods in this package are new, but the main method is based on a workflow from Meyer (2019) <doi:10.1016/j.ecolmodel.2019.108815> and Meyer (2022) <doi:10.1038/s41467-022-29838-9> , however, it was generalized and adjusted in the context of this package.

This package implements a new method ClussCluster descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, ClussCluster incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells.

This package implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

This package provides functions for calculating the conditional power for different models in survival time analysis within randomized clinical trials with two different treatments to be compared and survival as an endpoint.

This package provides a compilation of tests for hypotheses regarding covariance and correlation matrices for one or more groups. The hypothesis can be specified through a corresponding hypothesis matrix and a vector or by choosing one of the basic hypotheses, while for the structure test, only the latter works. Thereby Monte-Carlo and Bootstrap-techniques are used, and the respective method must be chosen, and the functions provide p-values and mostly also estimators of calculated covariance matrices of test statistics. For more details on the methodology, see Sattler et al. (2022) <doi:10.1016/j.jspi.2021.12.001>, Sattler and Pauly (2024) <doi:10.1007/s11749-023-00906-6>, and Sattler and Dobler (2025) <doi:10.48550/arXiv.2310.11799>.

An API wrapper for Cryptowatch to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See <https://docs.cryptowat.ch/rest-api> for a detailed documentation.

Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.

Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.

Create rich command line applications, with colors, headings, lists, alerts, progress bars, etc. It uses CSS for custom themes. This package is now superseded by the cli package. Please use cli instead in new projects.

Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.

This package provides six variants of two-way correspondence analysis (ca): simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca, singly ordered non symmetrical ca, and doubly ordered non symmetrical ca.

This package provides functions to simplify the process of preparing event and transaction for cohort analysis.

Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.

We unify various nonparametric hypothesis testing problems in a framework of permutation testing, enabling hypothesis testing on multi-sample, multidimensional data and contingency tables. Most of the functions available in the R environment to implement permutation tests are single functions constructed for specific test problems; to facilitate the use of the package, the package encapsulates similar tests in a categorized manner, greatly improving ease of use. We will all provide functions for self-selected permutation scoring methods and self-selected p-value calculation methods (asymptotic, exact, and sampling). For two-sample tests, we will provide mean tests and estimate drift sizes; we will provide tests on variance; we will provide paired-sample tests; we will provide correlation coefficient tests under three measures. For multi-sample problems, we will provide both ordinary and ordered alternative test problems. For multidimensional data, we will implement multivariate means (including ordered alternatives) and multivariate pairwise tests based on four statistics; the components with significant differences are also calculated. For contingency tables, we will perform permutation chi-square test or ordered alternative.