Utility functions for mutational signature analysis as described in Alexandrov, L. B. (2020) <doi:10.1038/s41586-020-1943-3>. This package provides two groups of functions. One is for dealing with mutational signature "exposures" (i.e. the counts of mutations in a sample that are due to each mutational signature). The other group of functions is for matching or comparing sets of mutational signatures. mSigTools stands for mutational Signature analysis Tools.

Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).

Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) self-exciting process, with given immigrant hazard rate functions and offspring density function. Calculate the likelihood of a MRHawkes process with given hazard rate functions and offspring density function for an (increasing) sequence of event times. Calculate the Rosenblatt residuals of the event times. Predict future event times based on observed event times up to a given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.

This package provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters. Provides a method for mapping US ZIP codes, including those of letter recipients. Provides a method for parsing and processing html code from online job postings of the American Political Science Association.

An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.

Concise and interpretable summaries for machine learning models and learners of the mlr3 ecosystem. The package takes inspiration from the summary function for (generalized) linear models but extends it to non-parametric machine learning models, based on generalization performance, model complexity, feature importances and effects, and fairness metrics.

This package provides functions to support data cleaning, evaluation, and description, developed for integration with Maelstrom Research software tools. madshapR provides functions primarily to evaluate and manipulate datasets and data dictionaries in preparation for data harmonization with the package Rmonize and to facilitate integration and transfer between RStudio servers and secure Opal environments. madshapR functions can be used independently but are optimized in conjunction with â Rmonizeâ functions for streamlined and coherent harmonization processing.

Implementation of adaptive assessment procedures based on Knowledge Space Theory (KST, Doignon & Falmagne, 1999 <ISBN:9783540645016>) and Formal Psychological Assessment (FPA, Spoto, Stefanutti & Vidotto, 2010 <doi:10.3758/BRM.42.1.342>) frameworks. An adaptive assessment is a type of evaluation that adjusts the difficulty and nature of subsequent questions based on the test taker's responses to previous ones. The package contains functions to perform and simulate an adaptive assessment. Moreover, it is integrated with two Shiny interfaces, making it both accessible and user-friendly. The package has been partially funded by the European Union - NextGenerationEU and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.5, project â RAISE - Robotics and AI for Socio-economic Empowermentâ (ECS00000035).

Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arXiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.

This package provides a simple and effective tool for computing and visualizing statistical power for meta-analysis, including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.

Algorithms to build set partitions and commutator matrices and their use in the construction of multivariate d-Hermite polynomials; estimation and derivation of theoretical vector moments and vector cumulants of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".

This package implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, with a focus on multivariate repeated measures and functional data. The package provides two approaches: (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional representations for exploratory analysis and visualization of dependence structures across multiple feature sets. The implementation follows the framework developed in Górecki, KrzyÅ ko, Gnettner and Kokoszka (2025) <doi:10.48550/arXiv.2510.04457>.

This package implements generalized maximum entropy estimation for linear regression, kink regression, and smooth transition kink regression models. The approach represents unknown parameters and disturbances as probability distributions over discrete support spaces and estimates them by maximizing entropy subject to model constraints. It is particularly suited to ill-posed problems and does not require distributional assumptions on the error term. The methods have been applied in empirical studies such as Tarkhamtham and Yamaka (2019) <https://thaijmath.com/index.php/thaijmath/article/view/867/870> and Maneejuk, Yamaka, and Sriboonchitta (2022) <doi:10.1080/03610918.2020.1836214>.

Create meta tags for R Markdown HTML documents and Shiny apps for customized social media cards, for accessibility, and quality search engine indexing. metathis currently supports HTML documents created with rmarkdown', shiny', xaringan', pagedown', bookdown', and flexdashboard'.

Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) <doi:10.5705/ss.202016.0117>.

This package provides fundamental functions for descriptive statistics, including MODE(), estimate_mode(), center_stats(), position_stats(), pct(), spread_stats(), kurt(), skew(), and shape_stats(), which assist in summarizing the center, spread, and shape of numeric data. For more details, see McCurdy (2025), "Introduction to Data Science with R" <https://jonmccurdy.github.io/Introduction-to-Data-Science/>.

This package implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) <https://www.proquest.com/docview/3218878972>.

Maximum likelihood estimation for series systems where the component cause of failure is masked. Implements analytical log-likelihood, score, and Hessian functions for exponential, homogeneous Weibull, and heterogeneous Weibull component lifetimes under masked cause conditions (C1, C2, C3). Supports exact, right-censored, left-censored, and interval-censored observations via composable observation functors. Provides random data generation, model fitting, and Fisher information for asymptotic inference. See Lin, Loh, and Bai (1993) <doi:10.1109/24.257799> and Craiu and Reiser (2006) <doi:10.1111/j.1541-0420.2005.00498.x>.

This package provides a suite of convenience functions for generating US state and county thematic maps using datasets from the MazamaSpatialUtils package.

Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.

Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.

Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301â 307 <doi:10.1111/2041-210X.14269>.

This package provides S7-based infrastructure for fitting mediation models, extracting path coefficients, and performing bootstrap inference. Designed as a foundation package for the mediation analysis ecosystem, supporting probmed', RMediation', and medrobust packages. Implements unified interfaces for model fitting across different engines (currently generalized linear models, with future support for mixed models and Bayesian methods), standardized extraction of mediation paths from various model types, and robust bootstrap inference methods. Mediation methods are described in MacKinnon, Lockwood and Williams (2004) <doi:10.1207/s15327906mbr3901_4>, Preacher and Hayes (2008) <doi:10.3758/brm.40.3.879>, Tofighi and MacKinnon (2011) <doi:10.3758/s13428-011-0076-x>, and VanderWeele (2014) <doi:10.1097/EDE.0000000000000121>.

This package provides a Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization algorithm. MADGRAD is a best-of-both-worlds optimizer with the generalization performance of stochastic gradient descent and at least as fast convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation is provided based on Defazio et al (2020) <doi:10.48550/arXiv.2101.11075>.