Implementation of a next-generation, multi-stock age-structured fisheries assessment model. multiSA is intended for use in mixed fisheries where stock composition can not be readily identified in fishery data alone, e.g., from catch and age/length composition. Models can be fitted to genetic data, e.g., stock composition of catches and close-kin pairs, with seasonal stock availability and movement.

Compute correlation, association, agreement, and reliability measures for small to high-dimensional datasets through a consistent matrix-oriented interface. Supports classical correlations (Pearson, Spearman, Kendall, Chatterjee's rank correlation), distance correlation, partial correlation with regularised estimators, shrinkage correlation for p >= n settings, robust correlations including biweight mid-correlation, percentage-bend, Winsorized, and skipped correlation, latent-variable methods for binary and ordinal data, pairwise and overall intraclass correlation for wide data, repeated-measures correlation, and agreement/reliability analyses based on Cohen's kappa, weighted kappa, multi-rater kappa, Gwet's AC1/AC2, Krippendorff's alpha, Bland-Altman methods, Lin's concordance correlation coefficient, Poisson GLMM concordance for count data, and repeated-measures intraclass/concordance correlation. Implemented with optimized C++ backends using BLAS/OpenMP and memory-aware symmetric updates, and returns standard R objects with print/summary/plot methods plus optional Shiny viewers for matrix inspection. Methods based on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>; high-dimensional shrinkage covariance estimation <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>; Wilcox (1994) <doi:10.1007/BF02294395>; Wilcox (2004) <doi:10.1080/0266476032000148821>; Hayes and Krippendorff (2007) <doi:10.1080/19312450709336664>; weighted repeated-measures correlation by Kondo et al. (2025) <doi:10.1002/sim.70046>.

This package provides comorbidity classification algorithms such as the Pediatric Complex Chronic Conditions (PCCC), Charlson, and Elixhauser indices, supports longitudinal comorbidity flagging across encounters, and includes utilities for working with medical coding schemas such as the International Classification of Diseases (ICD).

This package provides tools to help visualize Major League Baseball analysis in ggplot2 and gt'. You provide team/player information and mlbplotR will transform that information into team colors, logos, or player headshots for graphics.

This package implements likelihood inference based on higher order approximations for linear nonnormal regression models.

This package provides an interface to the Maxar Geospatial Platform (MGP) Application Programming Interface. <https://www.maxar.com/maxar-geospatial-platform> It facilitates imagery searches using the MGP Streaming Application Programming Interface via the Web Feature Service (WFS) method, and supports image downloads through Web Map Service (WMS) and Web Map Tile Service (WMTS) Open Geospatial Consortium (OGC) methods. Additionally, it integrates with the Maxar Geospatial Platform Basemaps Application Programming Interface for accessing Maxar basemaps imagery and seamlines. The package also offers seamless integration with the Maxar Geospatial Platform Discovery Application Programming Interface, allowing users to search, filter, and sort Maxar content, while retrieving detailed metadata in formats like SpatioTemporal Asset Catalog (STAC) and GeoJSON.

This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.

Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.

Implementation of a framework for cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiple imputed datasets while accounting for the uncertainty that the imputations introduce in the final results. In addition, the package can also be used for a cluster analysis of the complete cases of a single dataset. The package also includes specific methods to summarize and plot the results. The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.

This will allow easier management of a CRAN-style repository on local networks (i.e. not on CRAN). This might be necessary where hosted packages contain intellectual property owned by a corporation.

Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, mlmtools', is a suite of pre- and post-estimation tools for multilevel models in R'. Package implements post-estimation tools designed to work with models estimated using lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.

This package provides a set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix. These models have significant differences in terms of the analytical approach. Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075.

This package implements contamination bias diagnostics and alternative estimators for regressions with multiple treatments. The implementation is based on Goldsmith-Pinkham, Hull, and Kolesár (2024) <doi:10.48550/arXiv.2106.05024>.

Multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>, with support for multi-step adaptive MCP-net (MSAMNet) and multi-step adaptive SCAD-net (MSASNet) methods.

This package provides a framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.

This package provides access to MUI X Data Grid', a fast and extensible React data table and React data grid, with filtering, sorting, pagination, and more. Bundles the MIT-licensed community edition of the @mui/x-data-grid JavaScript library (the commercial Pro and Premium tiers are not included).

This package provides a comprehensive toolkit for missing person identification combining genetic and non-genetic evidence within a Bayesian framework. Computes likelihood ratios (LRs) for DNA profiles, biological sex, age, hair color, and birthdate evidence. Provides decision analysis tools including optimal LR thresholds, error rate calculations, and ROC curve visualization. Includes interactive Shiny applications for exploring evidence combinations. For methodological details see Marsico et al. (2023) <doi:10.1016/j.fsigen.2023.102891> and Marsico, Vigeland et al. (2021) <doi:10.1016/j.fsigen.2021.102519>.

Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.

Multiple 2 by 2 tables often arise in meta-analysis which combines statistical evidence from multiple studies. Two risks within the same study are possibly correlated because they share some common factors such as environment and population structure. This package implements a set of novel Bayesian approaches for multivariate meta analysis when the risks within the same study are independent or correlated. The exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 by 2 table or multiple 2 by 2 tables is provided. Luo, Chen, Su, Chu, (2014) <doi:10.18637/jss.v056.i11>, Chen, Luo, (2011) <doi:10.1002/sim.4248>, Chen, Chu, Luo, Nie, Chen, (2015) <doi:10.1177/0962280211430889>, Chen, Luo, Chu, Su, Nie, (2014) <doi:10.1080/03610926.2012.700379>, Chen, Luo, Chu, Wei, (2013) <doi:10.1080/19466315.2013.791483>.

Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.

Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.

To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number of assays needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate different orders in which the sample would be collected to form pools. Using MC avoids the dependence of the estimated number of assays on any specific ordering of the samples to form pools.

Compendium of the most representative algorithms in print---vector-valued dynamic programming, linear programming, policy iteration, the weighting factor approach---for solving multi-objective Markov decision processes, with or without reward discount, over a finite or infinite horizon. Mifrani, A. (2024) <doi:10.1007/s10479-024-06439-x>; Mifrani, A. & Noll, D. <doi:10.48550/arXiv.2502.13697>; Wakuta, K. (1995) <doi:10.1016/0304-4149(94)00064-Z>.

This package provides a graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.