Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d data. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. For longitudinal data, we also develop the GEE estimator to incorporate the within-subject correlations. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, ``Wei Zhong, Chuang Wan and Wenyang Zhang (2022). Estimation and inference for multikink quantile regression, JBES and ``Chuang Wan, Wei Zhong, Wenyang Zhang and Changliang Zou (2022). Multi-kink quantile regression for longitudinal data with application to progesterone data analysis, Biometrics".

Documentation at https://melpa.org/#/mu2tex

Documentation at https://melpa.org/#/mutant

Munch is a dot-accessible dictionary similar to JavaScript objects.

This package provides a multivariate generalization of the emulator package.

Partition a data frame across multiple worker processes to provide simple multicore parallelism.

This package provides functions to perform sensitivity analysis on a model with multivariate output.

When mutex_m is required, any object that extends or includes Mutex_m will be treated like a Mutex.

This package provides functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options.

Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.

cpp-mustache is a Mustache implementation for C++ 11 and above. It is header only and has zero dependencies. It provides a templated string type for compatibility with any STL-like string (std::string, std::wstring, etc).

An R package for deeping mining gene co-expression networks in multi-trait expression data. Provides functions for analyzing, comparing, and visualizing WGCNA networks across conditions. multiWGCNA was designed to handle the common case where there are multiple biologically meaningful sample traits, such as disease vs wildtype across development or anatomical region.

Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) <doi:10.1348/000711005X64817>, determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values.

Multivariate generalized Gaussian distribution, Multivariate Cauchy distribution, Multivariate t distribution. Distance between two distributions (see N. Bouhlel and A. Dziri (2019): <doi:10.1109/LSP.2019.2915000>, N. Bouhlel and D. Rousseau (2022): <doi:10.3390/e24060838>, N. Bouhlel and D. Rousseau (2023): <doi:10.1109/LSP.2023.3324594>). Manipulation of these multivariate probability distributions.

This package implements multitaper spectral estimation techniques using prolate spheroidal sequences (Slepians) and sine tapers for time series analysis. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.

Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).

This package provides a set of extensions for the ergm package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. ergm.multi is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2023) <doi:10.1080/01621459.2023.2242627>.

Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, <doi:10.48550/arXiv.2503.23158>).

Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) <http://www.jstor.org/stable/1392277> proposal.

This package provides tools used by organizational researchers for the analysis of multilevel data. It includes four broad sets of tools.

1. functions for estimating within-group agreement and reliability indices.

2. functions for manipulating multilevel and longitudinal (panel) data.

3. simulations for estimating power and generating multilevel data.

4. miscellaneous functions for estimating reliability and performing simple calculations and data transformations.

This package provides functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications.

Build CPMs (cumulative probability models, also known as cumulative link models) to account for detection limits (both single and multiple detection limits) in response variables. Conditional quantiles and conditional CDFs can be calculated based on fitted models. The package implements methods described in Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E. (2022). "Addressing Detection Limits with Semiparametric Cumulative Probability Models". <arXiv:2207.02815>.

Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.