Calculate Krippendorff's alpha for multi-valued data using the methods introduced by Krippendorff and Craggs (2016) <doi:10.1080/19312458.2016.1228863>. Nominal, ordinal, interval, and ratio data types are supported, with options to create bootstrapped estimates of alpha and/or parallelize calculations.

R interface to MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing MLflow', tracking experiments, creating and running projects, and saving and serving models.

This package provides functions for estimating structural equation models using instrumental variables.

It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.

It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>. Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>. Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>. Li et al. (2018) <doi:10.1177/0962280217692830>. We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.

Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>.

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>.

Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.

This package implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>.

This package provides a framework to factorise electromyography (EMG) data. Tools are provided for raw data pre-processing, non negative matrix factorisation, classification of factorised data and plotting of obtained outcomes. In particular, reading from ASCII files is supported, along with wide-used filtering approaches to process EMG data. All steps include one or more sensible defaults that aim at simplifying the workflow. Yet, all functions are largely tunable at need. Example data sets are included.

Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks, examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) ion sources.

This package provides supplemental functions for the mixRasch package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a plotting function to compare item parameters for multiple class models and a function that provides average theta values for each class in a mixture model.

Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053> and by Scutari (2010) <doi:10.18637/jss.v035.i03>.

Automation tool to run R scripts if needed, based on last modified time. It comes with no package dependencies, organizational overhead, or structural requirements. In short: run an R script if underlying files have changed, otherwise do nothing.

Uses the metadata information stored in metacore objects to check and build metadata associated columns.

Multivariate hypothesis tests and confidence intervals...

Simplifies Brazilian names phonetically using a custom metaphoneBR algorithm that preserves ending vowels. Useful for name matching processing preserving gender information carried generally by ending vowels in Portuguese. Mation (2025) <doi:10.6082/uchicago.15104>.

Takes a .state file generated by IQ-TREE as an input and, for each ancestral node present in the file, generates a FASTA-formatted maximum likelihood (ML) sequence as well as an âAltAllâ sequence in which uncertain sites, determined by the two parameters thres_1 and thres_2, have the maximum likelihood state swapped with the next most likely state as described in Geeta N. Eick, Jamie T. Bridgham, Douglas P. Anderson, Michael J. Harms, and Joseph W. Thornton (2017), "Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty" <doi:10.1093/molbev/msw223>.

This package provides a simple and effective tool for computing and visualizing statistical power for meta-analysis, including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.

Fast manipulation of symbolic multivariate polynomials using the Map class of the Standard Template Library. The package uses print and coercion methods from the mpoly package but offers speed improvements. It is comparable in speed to the spray package for sparse arrays, but retains the symbolic benefits of mpoly'. To cite the package in publications, use Hankin 2022 <doi:10.48550/ARXIV.2210.15991>. Uses disordR discipline.

Count data is prevalent and informative, with widespread application in many fields such as social psychology, personality, and public health. Classical statistical methods for the analysis of count outcomes are commonly variants of the log-linear model, including Poisson regression and Negative Binomial regression. However, a typical problem with count data modeling is inflation, in the sense that the counts are evidently accumulated on some integers. Such an inflation problem could distort the distribution of the observed counts, further bias estimation and increase error, making the classic methods infeasible. Traditional inflated value selection methods based on histogram inspection are easy to neglect true points and computationally expensive in addition. Therefore, we propose a multiple-inflated negative binomial model to handle count data modeling with multiple inflated values, achieving data-driven inflated value selection. The proposed approach provides simultaneous identification of important regression predictors on the target count response as well. More details about the proposed method are described in Li, Y., Wu, M., Wu, M., & Ma, S. (2023) <arXiv:2309.15585>.

This package provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.

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>).