This package contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).

Distributions that are typically used for exposure rating in general insurance, in particular to price reinsurance contracts. The vignette shows code snippets to fit the distribution to empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208> freely available on-line.

This package provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.

This package provides a method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury, Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.

This package implements Mander & Thompson's (2010) <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. Also provides an implementation of Simon's (1989) <doi:10.1016/0197-2456(89)90015-9> original methodology and allows exploration of the operating characteristics of sub-optimal designs.

An R interface to version 0.3 of the ROPTLIB optimization library (see <https://www.math.fsu.edu/~whuang2/> for more information). Optimize real- valued functions over manifolds such as Stiefel, Grassmann, and Symmetric Positive Definite matrices. For details see Martin et. al. (2020) <doi:10.18637/jss.v093.i01>. Note that the optional ldr package used in some of this package's examples can be obtained from either JSS <https://www.jstatsoft.org/index.php/jss/article/view/v061i03/2886> or from the CRAN archives <https://cran.r-project.org/src/contrib/Archive/ldr/ldr_1.3.3.tar.gz>.

This package provides basic tools and wrapper functions for computing clusters of instances described by multiple time-to-event censored endpoints. From long-format datasets, where one instance is described by one or more dated records, the main function, `make_state_matrices()`, creates state matrices. Based on these matrices, optimised procedures using the Jaccard distance between instances enable the construction of longitudinal typologies. The package is under active development, with additional tools for graphical representation of typologies planned. For methodological details, see our accompanying paper: `Delord M, Douiri A (2025) <doi:10.1186/s12874-025-02476-7>`.

This package provides a toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.

Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.

This package provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks.

Automatically estimate 11 effect size measures from a well-formatted dataset. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.

The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials.

Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2018) <doi:10.1093/bioinformatics/btx835>). This package aims to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest.

Compute correlation and other association matrices from small to high-dimensional datasets with relative simple functions and sensible defaults. Includes options for shrinkage and robustness to improve results in noisy or high-dimensional settings (p >= n), plus convenient print/plot methods for inspection. Implemented with optimised C++ backends using BLAS/OpenMP and memory-aware symmetric updates. Works with base matrices and data frames, returning standard R objects via a consistent S3 interface. Useful across genomics, agriculture, and machine-learning workflows. Supports Pearson, Spearman, Kendall, distance correlation, partial correlation, and robust biweight mid-correlation; Blandâ Altman analyses and Lin's concordance correlation coefficient (including repeated-measures extensions). Methods based on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>; Schäfer and Strimmer (2005) <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>.

Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.

Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, MBMethPred can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.

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

Basic Setup for Projects in R for Monterey County Office of Education. It contains functions often used in the analysis of education data in the county office including seeing if an item is not in a list, rounding in the manner the general public expects, including logos for districts, switching between district names and their county-district-school codes, accessing the local SQL table and making thematically consistent graphs.

Create legends for maps and other graphics. Thematic maps need to be accompanied by legible legends to be fully comprehensible. This package offers a wide range of legends useful for cartography, some of which may also be useful for other types of graphics.

Generate the monotonic binning and perform the woe (weight of evidence) transformation for the logistic regression used in the consumer credit scorecard development. The woe transformation is a piecewise transformation that is linear to the log odds. For a numeric variable, all of its monotonic functional transformations will converge to the same woe transformation.

This package implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.

This package provides a hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package MajMinKmeans are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator.

This package provides a framework for multipurpose optimal resource allocation in survey sampling, extending the classical optimal allocation principles introduced by Tschuprow (1923) and Neyman (1934) to multidomain and multivariate allocation problems. The primary method mosalloc() allows for the consideration of precision and cost constraints at the subpopulation level while minimizing either a vector of sampling errors or survey costs across a broad range of optimal sample allocation problems. The approach supports both single- and multistage designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user- friendly interface. Sensitivity analysis is supported through the problem's dual variables, which are naturally obtained via the internal use of the Embedded Conic Solver from the ECOSolveR package. See Willems (2025, <doi:10.25353/ubtr-9200-484c-5c89>) for a detailed description of the theory behind MOSAlloc'.

This package provides tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets. For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).