The zathura-pdf-mupdf plugin adds PDF support to zathura by using the mupdf rendering library.

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

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

This module adds the ability to set vCard for MUC rooms. One of the most common use cases is to define avatars for MUC rooms.

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.

The package enables the user to typeset version control information provided by RCS keywords (e.g., $ID: ... $) in LaTeX documents that contain multiple TeX files.

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.

Effect sizes, diagnostics and performance metrics for multilevel and mixed effects models. Includes marginal and conditional R2 estimates for linear mixed effects models based on Johnson (2014) <doi:10.1111/2041-210X.12225>.

This package implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.

Package provides mutations datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. Mutations data format is explained here https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+(MAF)+Specification. There is extra one column with patients barcodes. Data from 2015-11-01 snapshot.

This package lets you typeset keywords of the version control system Subversion inside your LaTeX files anywhere you like. Unlike the otherwise similar package svn, the use of multiple files for one LaTeX document is well supported. The package interacts with an external Perl script, to retrieve information necessary for the required output.

This package provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.

This package provides methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB. Includes ISCC-NBS color block lookup. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".

Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.

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.

multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.

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.