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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-mashr 0.2.79
Propagated dependencies: r-softimpute@1.4-3 r-rmeta@3.0 r-rcppgsl@0.3.13 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9 r-mvtnorm@1.3-3 r-assertthat@0.2.1 r-ashr@2.2-63 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/stephenslab/mashr
Licenses: Modified BSD
Build system: r
Synopsis: Multivariate Adaptive Shrinkage
Description:

This package implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.

r-msos 1.2.0
Propagated dependencies: r-tree@1.0-45 r-mclust@6.1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/coatless/msos
Licenses: Expat
Build system: r
Synopsis: Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden
Description:

Multivariate Analysis methods and data sets used in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>. This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC').

r-mexplorer 1.0.0
Propagated dependencies: r-nnet@7.3-20
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mExplorer
Licenses: GPL 2+
Build system: r
Synopsis: Identifying Master Gene Regulators from Gene Expression and DNA-Binding Data
Description:

The method m:Explorer associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.

r-masswater 2.2.1
Propagated dependencies: r-writexl@1.5.4 r-units@1.0-0 r-tidyterra@1.0.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-sf@1.0-23 r-rmarkdown@2.30 r-readxl@1.4.5 r-rcolorbrewer@1.1-3 r-maptiles@0.11.0 r-lubridate@1.9.4 r-httr@1.4.7 r-ggspatial@1.1.10 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-flextable@0.9.10 r-dplyr@1.1.4 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: <https://github.com/massbays-tech/MassWateR>
Licenses: CC0
Build system: r
Synopsis: Quality Control and Analysis of Massachusetts Water Quality Data
Description:

This package provides methods for quality control and exploratory analysis of surface water quality data collected in Massachusetts, USA. Functions are developed to facilitate data formatting for the Water Quality Exchange Network <https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting of data quality objectives to state agencies. Quality control methods are from Massachusetts Department of Environmental Protection (2020) <https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.

r-manyivsnets 0.1.1
Propagated dependencies: r-sandwich@3.1-1 r-readr@2.1.6 r-magrittr@2.0.4 r-lmtest@0.9-40 r-igraph@2.2.1 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-aer@1.2-15
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/avishekb9/ManyIVsNets
Licenses: Expat
Build system: r
Synopsis: Environmental Phillips Curve Analysis with Multiple Instrumental Variables and Networks
Description:

Comprehensive toolkit for Environmental Phillips Curve analysis featuring multidimensional instrumental variable creation, transfer entropy causal discovery, network analysis, and state-of-the-art econometric methods. Implements geographic, technological, migration, geopolitical, financial, and natural risk instruments with robust diagnostics and visualization. Provides 24 different instrumental variable approaches with empirical validation. Methods based on Phillips (1958) <doi:10.1111/j.1468-0335.1958.tb00003.x>, transfer entropy by Schreiber (2000) <doi:10.1103/PhysRevLett.85.461>, and weak instrument tests by Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006>.

r-molhd 0.2
Propagated dependencies: r-fields@17.1 r-arrangements@1.1.9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MOLHD
Licenses: LGPL 2.0+
Build system: r
Synopsis: Multiple Objective Latin Hypercube Design
Description:

Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.

r-mcgibbsit 1.2.2
Propagated dependencies: r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/r-gregmisc/mcgibbsit
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Warnes and Raftery's 'MCGibbsit' MCMC Run Length and Convergence Diagnostic
Description:

Implementation of Warnes & Raftery's MCGibbsit run-length and convergence diagnostic for a set of (not-necessarily independent) Markov Chain Monte Carlo (MCMC) samplers. It combines the quantile estimate error-bounding approach of the Raftery and Lewis MCMC run length diagnostic `gibbsit` with the between verses within chain approach of the Gelman and Rubin MCMC convergence diagnostic.

r-multipleoutcomes 0.4
Propagated dependencies: r-survival@3.8-3 r-stringr@1.6.0 r-numderiv@2016.8-1.1 r-momentfit@1.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multipleOutcomes
Licenses: Expat
Build system: r
Synopsis: Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes
Description:

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.

r-msrdt 0.1.0
Propagated dependencies: r-reshape2@1.4.5 r-gtools@3.9.5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ericchen12377/MSRDT
Licenses: GPL 3
Build system: r
Synopsis: Multi-State Reliability Demonstration Tests (MSRDT)
Description:

This is a implementation of design methods for multi-state reliability demonstration tests (MSRDT) with failure count data, which is associated with the work from the published paper "Multi-state Reliability Demonstration Tests" by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>. It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively. It also provides the implementation of conventional design method, namely binomial tests for failure count data.

r-mdsmap 1.3
Propagated dependencies: r-smacof@2.1-7 r-rgl@1.3.31 r-reshape@0.8.10 r-princurve@2.1.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MDSMap
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: High Density Genetic Linkage Mapping using Multidimensional Scaling
Description:

Estimate genetic linkage maps for markers on a single chromosome (or in a single linkage group) from pairwise recombination fractions or intermarker distances using weighted metric multidimensional scaling. The methods are suitable for autotetraploid as well as diploid populations. Options for assessing the fit to a known map are also provided. Methods are discussed in detail in Preedy and Hackett (2016) <doi:10.1007/s00122-016-2761-8>.

r-mbrm 0.1.1
Propagated dependencies: r-tibble@3.3.0 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-formula@1.2-5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MBRM
Licenses: GPL 3
Build system: r
Synopsis: Mixed Regression Models with Generalized Log-Gamma Random Effects
Description:

Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. Estimation is performed by maximizing the log-likelihood using numerical optimization techniques (Lizandra C. Fabio, Vanessa Barros, Cristian Lobos, Jalmar M. F. Carrasco, Marginal multivariate approach: A novel strategy for handling correlated binary outcomes, 2025, under submission).

r-min2halfffd 0.1.0
Propagated dependencies: r-shinybusy@0.3.3 r-shiny@1.11.1 r-hrtlfmc@0.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=min2HalfFFD
Licenses: GPL 3
Build system: r
Synopsis: Minimally Changed Two-Level Half-Fractional Factorial Designs
Description:

In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) <doi:10.1080/03610926.2016.1152490>. This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface.

r-mlegp 3.1.10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/gdancik/mlegp/
Licenses: GPL 2+
Build system: r
Synopsis: Maximum Likelihood Estimates of Gaussian Processes
Description:

Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>. Contact the maintainer for a package version that includes sensitivity analysis.

r-mlgl 1.0.1
Propagated dependencies: r-paralleldist@0.2.7 r-matrix@1.7-4 r-mass@7.3-65 r-gglasso@1.6 r-fastcluster@1.3.0 r-factominer@2.12
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MLGL
Licenses: GPL 2+
Build system: r
Synopsis: Multi-Layer Group-Lasso
Description:

It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).

r-modgo 1.0.1
Propagated dependencies: r-wesanderson@0.3.7 r-survival@3.8-3 r-psych@2.5.6 r-patchwork@1.3.2 r-matrix@1.7-4 r-mass@7.3-65 r-gridextra@2.3 r-gp@1.1 r-gldex@2.0.0.9.4 r-ggplot2@4.0.1 r-ggcorrplot@0.1.4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=modgo
Licenses: GPL 3
Build system: r
Synopsis: Mock Data Generation
Description:

Generation of synthetic data from a real dataset using the combination of rank normal inverse transformation with the calculation of correlation matrix <doi:10.1055/a-2048-7692>. Completely artificial data may be generated through the use of Generalized Lambda Distribution and Generalized Poisson Distribution <doi:10.1201/9781420038040>. Quantitative, binary, ordinal categorical, and survival data may be simulated. Functionalities are offered to generate synthetic data sets according to user's needs.

r-mmd 1.0.0
Propagated dependencies: r-plyr@1.8.9 r-e1071@1.7-16 r-bigmemory@4.6.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MMD
Licenses: GPL 3
Build system: r
Synopsis: Minimal Multilocus Distance (MMD) for Source Attribution and Loci Selection
Description:

The aim of the package is two-fold: (i) To implement the MMD method for attribution of individuals to sources using the Hamming distance between multilocus genotypes. (ii) To select informative genetic markers based on information theory concepts (entropy, mutual information and redundancy). The package implements the functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to efficiently attribute individuals to source populations. Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>. See more details and examples in the README file.

r-mcmc-qpcr 1.2.4
Propagated dependencies: r-mcmcglmm@2.36 r-ggplot2@4.0.1 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MCMC.qpcr
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Analysis of qRT-PCR Data
Description:

Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.

r-micromodal 1.0.0
Propagated dependencies: r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/kennedymwavu/micromodal
Licenses: Expat
Build system: r
Synopsis: Create Simple and Elegant Modal Dialogs in 'shiny'
Description:

Enables you to create accessible modal dialogs, with confidence and with minimal configuration.

r-mazealls 0.2.1
Propagated dependencies: r-turtlegraphics@1.0-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/shabbychef/mazealls
Licenses: LGPL 3
Build system: r
Synopsis: Generate Recursive Mazes
Description:

Supports the generation of parallelogram, equilateral triangle, regular hexagon, isosceles trapezoid, Koch snowflake, hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski trapezoid mazes via TurtleGraphics'. Mazes are generated by the recursive method: the domain is divided into sub-domains in which mazes are generated, then dividing lines with holes are drawn between them, see J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.

r-mom 0.1.0
Propagated dependencies: r-vgam@1.1-13 r-actuar@3.3-6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MOM
Licenses: Expat
Build system: r
Synopsis: Estimation and Testing of Hypothesis
Description:

This package provides a collection of functions to do some statistical inferences. On estimation, it has the function to get the method of moments estimates, the sampling interval. In terms of testing it has function of doing most powerful test.

r-minimap 0.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://github.com/seankross/minimap
Licenses: Expat
Build system: r
Synopsis: Create Tile Grid Maps
Description:

Create tile grid maps, which are like choropleth maps except each region is represented with equal visual space.

r-mipfp 3.2.1
Propagated dependencies: r-rsolnp@2.0.1 r-numderiv@2016.8-1.1 r-cmm@1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/jojo-/mipfp
Licenses: GPL 2
Build system: r
Synopsis: Multidimensional Iterative Proportional Fitting and Alternative Models
Description:

An implementation of the iterative proportional fitting (IPFP), maximum likelihood, minimum chi-square and weighted least squares procedures for updating a N-dimensional array with respect to given target marginal distributions (which, in turn can be multidimensional). The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions.

r-mmpa 1.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mMPA
Licenses: Expat
Build system: r
Synopsis: Implementation of Marker-Assisted Mini-Pooling with Algorithm
Description:

To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number of assays needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate different orders in which the sample would be collected to form pools. Using MC avoids the dependence of the estimated number of assays on any specific ordering of the samples to form pools.

r-mertools 0.6.4
Propagated dependencies: r-shiny@1.11.1 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-lme4@1.1-37 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-broom-mixed@0.2.9.7 r-blme@1.0-6 r-arm@1.14-4 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=merTools
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Analyzing Mixed Effect Regression Models
Description:

This package provides methods for extracting results from mixed-effect model objects fit with the lme4 package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models. This method draws from the simulation framework used in the Gelman and Hill (2007) textbook: Data Analysis Using Regression and Multilevel/Hierarchical Models.

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