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

This package provides retain_mut method that has the same functionality as retain but gives mutable borrow to the predicate.

This package provides retain_mut method that has the same functionality as retain but gives mutable borrow to the predicate.

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.

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).

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

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.

multi_test provides a uniform interface onto whatever testing libraries that have been loaded into a running Ruby process to help control rogue test/unit/autorun requires.

This package provides a common interface to multiple JSON libraries, including Oj, Yajl, the JSON gem (with C-extensions), the pure-Ruby JSON gem, NSJSONSerialization, gson.rb, JrJackson, and OkJson.

Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).

Bindings for hierarchical regression models for use with the parsnip package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect models (Pinheiro and Bates) <doi:10.1007/978-1-4419-0318-1_1>.

This package provides an implementation of MurmurHash2, a good, fast, general-purpose, non-cryptographic hashing function. See https://sites.google.com/site/murmurhash/ for details. This implementation is pure Haskell, so it might be a bit slower than a C FFI binding.

The package provides the \multido command, which was originally designed for use with PSTricks. Fixed-point arithmetic is used when working on the loop variable, so that the package is equally applicable in graphics applications like PSTricks as it is with the more common integer loops.

This package provides the following binaries to drive the Linux Device Mapper multipathing driver:

1. multipath - Device mapper target autoconfig.

2. multipathd - Multipath daemon.

3. mpathpersist - Manages SCSI persistent reservations on dm multipath devices.

4. kpartx - Create device maps from partition tables.

The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences.

This package provides functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.

Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029).

This package provides a collection of tools for doing various analyses of multi-state QTL data, with a focus on visualization and interpretation. The package multistateQTL contains functions which can remove or impute missing data, identify significant associations, as well as categorise features into global, multi-state or unique. The analysis results are stored in a QTLExperiment object, which is based on the SummarisedExperiment framework.

Check concordance of a vector of mutation impacts with standard dictionaries such as Sequence Ontology (SO) <http://www.sequenceontology.org/>, Mutation Annotation Format (MAF) <https://docs.gdc.cancer.gov/Encyclopedia/pages/Mutation_Annotation_Format_TCGAv2/> or Prediction and Annotation of Variant Effects (PAVE) <https://github.com/hartwigmedical/hmftools/tree/master/pave>. It enables conversion between SO/PAVE and MAF terms and selection of the most severe consequence where multiple ampersand (&) delimited impacts are given.

This package is designed to simplify the development and distribution of scripts for theatrical musicals, especially ones under development. The output is formatted to follow generally accepted script style while also maintaining a high level of typographic integrity, and includes commands for dialog, lyrics, stage directions, music and dance cues, rehearsal marks, and more. It gracefully handles dialog that crosses page breaks, and can generate lists of songs and lists of dances in the show.

Matching algorithm based on network-flow structure. Users are able to modify the emphasis on three different optimization goals: two different distance measures and the number of treated units left unmatched. The method is proposed by Pimentel and Kelz (2019) <doi:10.1080/01621459.2020.1720693>. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

Meta-analyses can be compromised by studies internal biases (e.g., confounding in nonrandomized studies) as well as by publication bias. This package conducts sensitivity analyses for the joint effects of these biases (per Mathur (2022) <doi:10.31219/osf.io/u7vcb>). These sensitivity analyses address two questions: (1) For a given severity of internal bias across studies and of publication bias, how much could the results change?; and (2) For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount?