With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) <doi:10.1093/jrsssc/qlaf027>). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

This package implements three bias-correction techniques from Battaglia et al. (2025 <doi:10.48550/arXiv.2402.15585>) to improve inference in regression models with covariates generated by AI or machine learning.

This package creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.

DNA methylation is an epigenetic modification involved in genomic stability, gene regulation, development and disease. DNA methylation occurs mainly through the addition of a methyl group to cytosines, for example to cytosines in a CpG dinucleotide context (CpG stands for a cytosine followed by a guanine). Tissue-specific methylation patterns lead to genomic regions with different characteristic methylation levels. E.g. in vertebrates CpG islands (regions with high CpG content) that are associated to promoter regions of expressed genes tend to be unmethylated. MethEvolSIM is a model-based simulation software for the generation and modification of cytosine methylation patterns along a given tree, which can be a genealogy of cells within an organism, a coalescent tree of DNA sequences sampled from a population, or a species tree. The simulations are based on an extension of the model of Grosser & Metzler (2020) <doi:10.1186/s12859-020-3438-5> and allows for changes of the methylation states at single cytosine positions as well as simultaneous changes of methylation frequencies in genomic structures like CpG islands.

Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.

Multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.

This package provides a compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.

Analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.

This package implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 <doi:10.1002/sim.10222>) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) <doi:10.48550/arXiv.2512.19064>.

Query, extract, and plot genealogical data from The Mathematics Genealogy Project <https://mathgenealogy.org/>. Data is gathered from the WebSocket server run by the geneagrapher-core project <https://github.com/davidalber/geneagrapher-core>.

Train and make predictions from a multi-layer perceptron neural network with optional partial monotonicity constraints.

Analyzes adverse events in clinical trials using the metalite data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

Local recombination rates are graphically estimated across a genome using Marey maps.

Meta-analyses can be compromised by studies internal biases (e.g., confounding in nonrandomized studies) as well as by publication bias. This package conducts sensitivity analyses for the joint effects of these biases (per Mathur (2022) <doi:10.31219/osf.io/u7vcb>). These sensitivity analyses address two questions: (1) For a given severity of internal bias across studies and of publication bias, how much could the results change?; and (2) For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount?

This package provides methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.

Fit mixture of Markov chains of higher orders from multiple sequences. It is also compatible with ordinary 1-component, 1-order or single-sequence Markov chains. Various utility functions are provided to derive transition patterns, transition probabilities per component and component priors. In addition, print(), predict() and component extracting/replacing methods are also defined as a convention of mixture models.

This package provides a collection of functions to connect to a Moodle database, cache relevant tables locally and generate learning analytics. Moodle is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit <https://moodle.org>.

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) <doi:10.1101/gr.262675.120> and Chen et al. (2022) <doi:10.1186/s13059-022-02627-9> for associated statistical details.

Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and Tubo, B.F. (2024) <doi:10.24412/1932-2321-2024-278-115-123>].

This package provides functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022).

This package provides functions to calculate the minimum and maximum possible values of Cronbach's alpha when item-level missing data are present. Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011 <doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 <doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature.

This package provides methods for color labeling, calculation of eigengenes, merging of closely related modules.

This package provides an RStudio extension with a chat interface for an AI coding agent to help users with R programming tasks.

Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.