Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. Estimation is performed by maximizing the log-likelihood using numerical optimization techniques (Lizandra C. Fabio, Vanessa Barros, Cristian Lobos, Jalmar M. F. Carrasco, Marginal multivariate approach: A novel strategy for handling correlated binary outcomes, 2025, under submission).

This package provides functions to compute and plot multivariate (partial) Mantel correlograms.

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 the article <doi:10.18637/jss.v061.i03> or from the ldr package <https://cran.r-project.org/package=ldr>.

This package implements structural estimators to estimate preferences and correct for the sample selection bias of observed outcomes in matching markets. This includes one-sided matching of agents into groups (Klein, 2015) <doi:10.17863/CAM.5812> as well as two-sided matching of students to schools (Klein et al., 2024) <doi:10.1016/j.geb.2024.07.003>. The package also contains algorithms to find stable matchings in the three most common matching problems: the stable roommates problem (Irving, 1985) <doi:10.1016/0196-6774(85)90033-1>, the college admissions problem (Gale and Shapley, 1962) <doi:10.2307/2312726>, and the house allocation problem (Shapley and Scarf, 1974) <doi:10.1016/0304-4068(74)90033-0>.

This package provides functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.

This package provides density, distribution, quantile, and random generation functions for the Modified Half-Normal (MHN) distribution, along with moments and mode; the Fox-Wright Psi normalizing constant is evaluated internally. The MHN distribution arises as a conditional posterior in Bayesian Markov chain Monte Carlo and generalizes the half-normal, truncated normal, and square-root gamma distributions. Implements efficient sampling via the Sun, Kong & Pal (2023) <doi:10.1080/03610926.2021.1934700> algorithms and the Gao & Wang (2025) <doi:10.1080/03610918.2025.2524551> relaxed transformed density rejection method.

Estimation and comparison of the performances of diagnostic tests in multi-reader multi-case studies where true case statuses (or ground truths) are known and one or more readers provide test ratings for multiple cases. Reader performance metrics are provided for area under and expected utility of ROC curves, likelihood ratio of positive or negative tests, and sensitivity and specificity. ROC curves can be estimated empirically or with binormal or binormal likelihood-ratio models. Statistical comparisons of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and the unified framework of Hillis (2005) <doi:10.1002/sim.2024>. The ANOVA can be conducted with data from a full factorial, nested, or partially paired study design; with random or fixed readers or cases; and covariances estimated with the DeLong method, jackknifing, or an unbiased method. Smith and Hillis (2020) <doi:10.1117/12.2549075>.

Utility functions for discovering and managing metadata associated with spatially unique "known locations". Applications include all fields of environmental monitoring (e.g. air and water quality) where data are collected at stationary sites.

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.

Sixteen tools for bioinformatics processing and analysis of major histocompatibility complex (MHC) data. The functions are tailored for amplicon data sets that have been filtered using the dada2 method (for more information on dada2, visit <https://benjjneb.github.io/dada2/> ), but even other types of data sets can be analyzed. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates individual fasta files for each sample in the data set. The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in a data set, and mean distances of all pairwise comparisons within each sample in a data set. The function additionally outputs five tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for each amino acid position in all sequences in the data set. These tables may be useful for further downstream analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper for the kmeans() function of the stats package, which allows for bootstrapping. Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values do not produce clear inflection points ("elbows"). BootKmeans() performs multiple runs of kmeans() and estimates optimal k-values based on a user-defined threshold of BIC reduction. The method is an automated and bootstrapped version of visually inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool for evaluating whether different k-means() clustering models identify similar clusters, and summarize bootstrap model stats as means for different estimated values of k. It is designed to take files produced by the BootKmeans() function as input, but other data can be analyzed if the descriptions of the required data formats are observed carefully. The SynDist() function analyses of synonymous variation among aligned protein-coding DNA sequences, that is, nucleotide substitutions that do not translate to changes in the amino acid sequences due to degeneracy of the genetic code. The SynDist() function calculates synonymous nucleotide changes per base and per codon in pairwise sequence comparisons, as well as mean synonymous variation among all pairwise comparisons of the sequences within each sample in a data set. The PapaDiv() function compares parent pairs in the data set and calculate their joint MHC diversity, taking into account sequence variants that occur in both parents. The HpltFind() function infers putative haplotypes from families in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of the haplotype inference. The CreateHpltOccTable() function creates a binary (logical) haplotype-sequence occurrence matrix from the output of HpltFind(), for easy overview of which sequences are present in which haplotypes. The HpltMatch() function compares haplotypes to help identify overlapping and potentially identical types. The NestTablesXL() function translates the output from HpltFind() to an Excel workbook, that provides a convenient overview for evaluation and curating of the inferred putative haplotypes.

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".

Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.

This package provides spatially survey balanced designs. Information about the package itself is given in Foster (2021) <doi:10.1111/2041-210X.13535>. Designs using MBHdesign can: 1) accommodate, without substantial detrimental effects on spatial balance, legacy sites (Foster et al., 2017 <doi:10.1111/2041-210X.12782>); 2) be based on points or transects (foster et al. 2020 <doi:10.1111/2041-210X.13321> and produce clustered samples (Foster et al. (in press). The base idea that these designs stem from is the quasi-random number method described Robinson et al. (2013) <doi:10.1111/biom.12059> and adjusted in Robinson et al. (2017) <doi:10.1016/j.spl.2017.05.004>.

Automatic marking of R assignments for students and teachers based on testthat test suites.

This package provides a tidy, high-level interface for creating polished maps of Malawi at country, region, district, and Traditional Authority level. Functions handle spatial data retrieval, administrative-name matching, joins from ordinary data frames, numeric and categorical choropleths, labels, highlights, and professional ggplot2 styling. Spatial boundary data are provided by the companion package mwmapdata'.

The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

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>.

The administrative boundaries of Senegal are provided at several levels, including regions, departments, arrondissements and communes. The Global Administrative Areas database, or `GADM` <https://gadm.org/>, is the primary source for these layers. The dataset is complemented by the incorporation of additional geographic layers, such as localities, universities, roads, or health facility locations.

Multi-Dimensional Analysis (MDA) is an adaptation of factor analysis developed by Douglas Biber (1992) <doi:10.1007/BF00136979>. Its most common use is to describe language as it varies by genre, register, and use. This package contains functions for carrying out the calculations needed to describe and plot MDA results: dimension scores, dimension means, and factor loadings.

It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.

This package provides a set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: SMS-EMOA <doi:10.1016/j.ejor.2006.08.008> NSGA-III <doi:10.1109/TEVC.2013.2281535> MO-CMA-ES <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: DTLZ1'-'DTLZ4 from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and WFG4'-'WFG9 from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.

Fast moment-based hierarchical model fitting. Implements methods from the papers "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017) and "Fitting a Deeply Nested Hierarchical Model to a Large Book Review Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry (2018).

Aggregates matrix population models (MPMs) in both the lambda (stable growth rate) and R0 (net reproductive rate) frameworks, including standard and elasticity-consistent aggregators. Standard aggregation in the lambda framework maintains consistent lambda and stable stage distribution, while standard aggregation in the R0 framework maintains consistent R0 and cohort stable stage distribution. Elasticity-consistent aggregators maintain these same consistencies with respect to the chosen framework and additionally preserve consistent reproductive values in the lambda framework and cohort reproductive values in the R0 framework. Aggregation can take the form of general-to-general MPM (mpm_aggregate) or Leslie-to-Leslie MPM (leslie_aggregate).

Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting.