Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration.

Constructs trees for multivariate survival data using marginal and frailty models. Grows, prunes, and selects the best-sized tree.

This package performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and publication-ready output. MCMC output may be derived from Bayesian model output fit with Stan, NIMBLE, JAGS, and other software.

Offers automation tools to parallelize Mplus operations when using R for data generation. It facilitates streamlined integration between Mplus and R', allowing users to run and manage multiple Mplus models simultaneously and efficiently in R'.

Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>. It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.

This package performs and benchmarks various Multi-Criteria Decision Analysis (MCDA) methods. MCDA is a decision-making framework used to evaluate and rank alternatives based on multiple conflicting criteria using normalization, weighting, and aggregation techniques. The package implements a wide range of MCDA methods including ARAS (Additive Ratio Assessment), AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization), COCOSO (Combined Compromise Solution), CODAS (Combinative Distance-based Assessment), COPRAS (Complex Proportional Assessment), EDAS (Evaluation based on Distance from Average Solution), ELECTRE (Elimination and Choice Expressing Reality) family (I-IV), FUCA (Faire Un Choix Adequat), GRA (Grey Relational Analysis), MABAC (Multi-Attributive Border Approximation Area Comparison), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), MAUT (Multi-Attribute Utility Theory), MAVT (Multi-Attribute Value Theory), MEGAN (Multi-criteria Evaluation with Gradual-weighting and Aggregation of Normalized distance matrices), MOORA (Multi-Objective Optimization on the basis of Ratio Analysis), OCRA (Operational Competitiveness Rating Analysis), ORESTE (Organisation, Rangement Et Synthese De Donnees Relationnelles), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations I-VI), RAM (Root Assessment Method), ROV (Range of Value), SMART (Simple Multi-Attribute Rating Technique), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), WASPAS (Weighted Aggregated Sum Product Assessment), WPM (Weighted Product Model), and WSM (Weighted Sum Model). The package computes comparative evaluation measures including Spearman rank correlation (Spearman, 1904) <doi:10.2307/1412107>, Salabun-Urbaniak's weight similarity index (Salabun and Urbaniak, 2020)<doi:10.1007/978-3-030-50417-5_47>, Wilcoxon signed-rank test (Wilcoxon, 1945)<doi:10.2307/3001968>, and permutation- and bootstrap- based entropy difference tests for pairwise method comparisons using Jensen-Shannon divergence (Lin, 1991)<doi:10.1109/18.61115>. It also provides sensitivity and stability analysis of MCDA results. Weight sensitivity analysis is implemented through deterministic and stochastic perturbation of criterion weights, and is also integrated as a built-in step within the MEGAN method framework (Cebeci, 2026)<doi:10.7717/peerj-cs.3819>.

Fits matrix-variate variance-gamma (MVVG) and matrix-variate normal-inverse-Gaussian (MVNIG) linear regression models using expectation-conditional maximization (ECM) algorithms. The models accommodate clustered matrix-valued responses, with unequal numbers of observations across subjects, correlated responses, skewness, and within-subject dependence. Functions are provided for model fitting, prediction, and subject-level influence assessment using approximate generalized Cook's distances. The package also includes motivating periodontal data from Gullah-speaking African Americans with Type-II diabetes. For details on the underlying matrix-variate distributions (MVVG and MVNIG), see Gallaugher and McNicholas (2019, <doi:10.1016/j.spl.2018.08.012>).

Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>. This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix.

This package provides a set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication <doi:10.1007/978-1-4939-7819-9_20>.

Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.

Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) <doi:10.1002/hbm.26425>) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in inst/workflow/') is also implemented with high performance techniques.

Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the DescTools package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research.

This package provides tools for fitting spatial Mixed-Data Sampling (MIDAS) regression models using Integrated Nested Laplace Approximation (INLA) (Rue et al., 2009) <doi:10.1111/j.1467-9868.2008.00700.x>. The package is designed for settings where responses and explanatory variables are observed at different temporal frequencies and supports both constant and spatially varying regression coefficients.

This package implements a minimum-spanning-tree-based heuristic for k-means clustering using a union-find disjoint set and the algorithm in Kruskal (1956) <doi:10.1090/S0002-9939-1956-0078686-7>.

This package provides functions of marginal mean and quantile regression models are used to analyze environmental exposure and biomonitoring data with repeated measurements and non-detects (i.e., values below the limit of detection (LOD)), as well as longitudinal exposure data that include non-detects and time-dependent covariates. For more details see Chen IC, Bertke SJ, Curwin BD (2021) <doi:10.1038/s41370-021-00345-1>, Chen IC, Bertke SJ, Estill CF (2024) <doi:10.1038/s41370-024-00640-7>, Chen IC, Bertke SJ, Dahm MM (2024) <doi:10.1093/annweh/wxae068>, and Chen IC (2025) <doi:10.1038/s41370-025-00752-8>.

The sample mean and standard deviation are two commonly used statistics in meta-analyses, but some trials use other summary statistics such as the median and quartiles to report the results. Therefore, researchers need to transform those information back to the sample mean and standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.

Package with multivariate analysis methodologies for experiment evaluation. The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA, principal components, canonical variables, etc. (Pacote com metodologias de analise multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.).

Audits predictions from fitted machine learning models without requiring retraining or access to the fitted model object. It supports binary classification, multiclass classification, and regression, providing performance metrics with bootstrap confidence intervals, calibration and threshold analyses, residual diagnostics, subgroup comparisons, user-defined acceptance criteria, risk warnings, and self-contained reports. For binary-classification and regression models, the package can also assemble and render structured model cards documenting intended use, evaluation data, performance, subgroup results, assumptions, and limitations. The model-card reporting approach is described by Mitchell et al. (2019) "Model Cards for Model Reporting" <doi:10.1145/3287560.3287596>.

This package provides tools for univariate and multivariate generalized linear models with model averaging and null model technique.

This package provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) <doi:10.1007/s40617-025-01058-9>.

Build multiscalar territorial analysis based on various contexts.

This package provides a suite of tools to allow you to download all publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from the Malaria Atlas Project <https://malariaatlas.org/> servers as well as utility functions for plotting the downloaded data.

Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous. Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile. You can find more information and validation in the Open Access paper. Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.

Fit and simulate mixtures of von Mises-Fisher distributions.