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This package makes available the most commonly used symbols in writing about music in a way that can be used with pdfLaTeX and looks consistent and attractive. It includes accidentals, meters, and notes of different rhythmic values. The package builds on the approach used in the harmony package, where the symbols are taken from the MusiXTeX fonts. But it provides a larger range of symbols and a more flexible, user-friendly interface.

This package provides tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.

The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the training set and applying the model in the test set (Solanes et al., Neuroimage 2023, 265:119800). This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes.

Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879>, Qin, Hong, Deutsch, and Bein (2019) <doi:10.1111/rssa.12446>, and Qin, Deutsch, and Hong (2021) <doi:10.1002/pam.22268>. It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias.

Multidimensional disables multidimensional array emulation.

This package provides Insertion ordered multimap.

Adds Grape style patterns to Mustermman

This package allows generating several versions of the same document for different audiences.

This package provides several commands for generating footnotes with multiple numbers (resp., marks).

This package provides the Network.Multicast Haskell module for sending UDP datagrams over multicast (class D) addresses.

Musicbrainzngs implements Python bindings of the MusicBrainz web service. This library can be used to retrieve music metadata from the MusicBrainz database.

This package provides the meson command, implemented as a symbolic link to the muon command of muon package.

This module adds multi-value syntax to selections and transitions, allowing you to set multiple attributes, styles or properties simultaneously with more concise syntax.

Mupen64Plus is a cross-platform plugin-based Nintendo 64 (N64) emulator which is capable of accurately playing many games. This package contains the Z64 video plugin.

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

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

This package contains a multicore Barnes-Hut implementation of the t-SNE algorithm. The implementation is described here: http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf.

Retrieve a scale based on a given mode and starting note. Information about these scales can be found on Wikipedia.

The iterative procedure estimates structural changes in the success probability of Bernoulli variables. It estimates the number and location of the breakpoints as well as the success probability of the different sequences between the breakpoints. In addition, it provides a graphical illustration of the result.

multipart-parser is a simple parser for multipart MIME messages, written in Ruby, based on felixge/node-formidable's parser. It has the following characteristics:

• Pure Ruby

• Event-driven API

• Only supports one level of multipart parsing

• Does not perform I/O

• Does not depend on any other library.

The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and MioÄ eviÄ (2024) <doi:10.3758/s13428-023-02079-4>. Support for Bayesian estimation using brms comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented.

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