This package provides methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) <DOI:10.1007/s11634-023-00547-5>. These models are estimated under a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>. In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>.

aarch64-linux-musl cross-compiler

Documentation at https://melpa.org/#/multiple-cursors

Documentation at https://melpa.org/#/projection-multi

Documentation at https://melpa.org/#/ebdb-mua-sidecar

This Ruby gem extends Faraday to perform multipart-post requests.

This package adds support to Emacs for editing text with multiple simultaneous cursors.

MUMPS (MUltifrontal Massively Parallel sparse direct Solver) solves a sparse system of linear equations A x = b using Gaussian elimination.

The package provides a series of operators commonly used in papers related to multiobjective optimisation, multiobjective evolutionary algorithms, multicriteria decision making and similar fields.

The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in radiant.data'.

MultiVolumefile is a Python library that provides a file-object abstraction, making it possible to use multiple files as if they were a single file.

Mupen64Plus is a cross-platform plugin-based Nintendo 64 (N64) emulator which is capable of accurately playing many games. This package contains the Rice Video plugin.

These are fonts for use with MusixTeX; they are provided both as original Metafont source, and as converted Adobe Type 1. The bundle renders the older (Type 1 fonts only) bundle musixtex-t1fonts obsolete.

Mupen64Plus is a cross-platform plugin-based Nintendo 64 (N64) emulator which is capable of accurately playing many games. This package contains the command line user interface. Installing this package is the easiest way towards a working Mupen64Plus for casual users.

MultiAssayExperiment harmonizes data management of multiple assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames.

Run Paris Agreement Capital Transition Assessment ('PACTA') analyses on multiple loan books in a structured way. Provides access to standard PACTA metrics and additional PACTA'-related metrics for multiple loan books. Results take the form of csv files and plots and are exported to user-specified project paths.

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.).

MultimodalExperiment is an S4 class that integrates bulk and single-cell experiment data; it is optimally storage-efficient, and its methods are exceptionally fast. It effortlessly represents multimodal data of any nature and features normalized experiment, subject, sample, and cell annotations, which are related to underlying biological experiments through maps. Its coordination methods are opt-in and employ database-like join operations internally to deliver fast and flexible management of multimodal data.

It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The MultiGroupSequential package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the graphical procedure proposed by Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>. Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013).

Documentation at https://melpa.org/#/mu4e-conversation

Documentation at https://melpa.org/#/mu4e-column-faces

Documentation at https://melpa.org/#/mu4e-marker-icons

Documentation at https://melpa.org/#/fix-muscle-memory

Documentation at https://melpa.org/#/mu4e-jump-to-list