Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

Mutagen is a Python module to handle audio metadata. It supports ASF, FLAC, M4A, Monkey’s Audio, MP3, Musepack, Ogg FLAC, Ogg Speex, Ogg Theora, Ogg Vorbis, True Audio, WavPack and OptimFROG audio files. All versions of ID3v2 are supported, and all standard ID3v2.4 frames are parsed. It can read Xing headers to accurately calculate the bitrate and length of MP3s. ID3 and APEv2 tags can be edited regardless of audio format. It can also manipulate Ogg streams on an individual packet/page level.

This package provides a collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.

This package implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>. Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42â 56, <doi:10.1037/a0021150>) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26â 40. <doi:10.1016/j.cogpsych.2017.05.003>).

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

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

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

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

This package permits easily typesetting arithmetical restorations using LaTeX.

This package provides a package containing an environment representing the Mu6500subA.CDF file.

This package provides a package containing an environment representing the Mu6500subB.CDF file.

This package provides a package containing an environment representing the Mu6500subD.CDF file.

This package provides a package containing an environment representing the Mu6500subC.CDF file.

emacs-multitran is a zero-dependency Emacs interface to the https://multitran.com online dictionary.

emacs-multitran is a zero-dependency Emacs interface to the https://multitran.com online dictionary.

Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.

Given a string pattern, Mustermann will turn it into an object that behaves like a regular expression and has comparable performance characteristics.

The mucproc.cls is a document class to support the formatting guidelines for submissions to the German Mensch und Computer conference.

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

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

This is a Common Lisp implementation for the Mustache template system. More details on the standard are available at https://mustache.github.io.

Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).

Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Ilich et al. (2023) <doi:10.1111/tgis.13067>).

Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum.