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.

Maintaining a LaTeX document with translations for multiple languages can be cumbersome and error-prone. This package provides a set of macros for defining macros and environments as wrappers around existing macros and environments. These wrappers allow one to clearly specify multiple translations for the arguments to the wrapped macros and environments while only the translation of the document's language is actually shown. Choosing a translation then is as simple as choosing the document's language via Babel or Polyglossia.

This package provides the @muladd macro. It automatically converts expressions with multiplications and additions or subtractions to calls with muladd which then fuse via FMA when it would increase the performance of the code. The @muladd macro can be placed on code blocks and it will automatically find the appropriate expressions and nest muladd expressions when necessary. In mixed expressions summands without multiplication will be grouped together and evaluated first but otherwise the order of evaluation of multiplications and additions is not changed.

The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71â 80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.

This package provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.

An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes are able to be calculated for either null model, and subsequently classified into one of four different interaction classifications (e.g., antagonistic or synergistic interactions). Analyses can be conducted on data for single experiments through to large meta-analytical datasets. Minimal input (or statistical knowledge) is required, with any output easily understood. Summary figures are also able to be easily generated.

This package provides a toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on switchBox package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret. For complex problems RF performs better. Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...). Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model. We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) <doi:10.1093/bioinformatics/btab088>.

This module provides utilities for multiplexing interactions with lists of Python objects.

MultiWriter can be used to write an ISO file to multiple USB devices at once.

The bundle offers a thesis class, based on memoir, that complies with Marquette University Graduate School requirements.

This package implements an algorithm for the spelling of enharmonics and dealing with ties and dots in rhythm notation.

This Common Lisp package offers an implementation of the 32-bit variant of MurmurHash3 (https://github.com/aappleby/smhasher), a fast non-crytographic hashing algorithm.

This package provides a decorator for adding multiple argument dispatching to functions. The decorator creates a multimethod object as needed and registers the function with its annotations.

murmurhash3 is a Python library for MurmurHash (MurmurHash3), a set of fast and robust hash functions. This library is a Python extension module written in C.

This is a Common Lisp library to change the capitalization and spacing of a string or a symbol. It can convert to and from Lisp, english, underscore and camel-case rules.

MutatorMath is a Python library for the calculation of piecewise linear interpolations in n-dimensions with any number of masters. It was developed for interpolating data related to fonts, but if can handle any arithmetic object.

Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple comparison adjustment, are illustrated.

This package provides tools to create a layout for figures made of multiple panels, and to fill the panels with base, lattice', ggplot2 and ComplexHeatmap plots, grobs, as well as content from all image formats supported by ImageMagick (accessed through magick').

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

Documentation at https://melpa.org/#/mustard-theme

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

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

Documentation at https://melpa.org/#/mustache-mode

Documentation at https://melpa.org/#/mustang-theme