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    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
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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-mapperalgo 1.0.9
Propagated dependencies: r-webshot2@0.1.2 r-viridislite@0.4.2 r-rlang@1.1.6 r-ppclust@1.1.0.1 r-networkd3@0.4.1 r-jsonlite@2.0.0 r-inaparc@1.2.1 r-igraph@2.2.1 r-htmlwidgets@1.6.4 r-ggplot2@4.0.1 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/TDA-R/MapperAlgo
Licenses: Expat
Build system: r
Synopsis: Topological Data Analysis: Mapper Algorithm
Description:

The Mapper algorithm from Topological Data Analysis, the steps are as follows 1. Define a filter (lens) function on the data. 2. Perform clustering within each level set. 3. Generate a complex from the clustering results.

r-multiplestressr 0.1.1
Propagated dependencies: r-viridis@0.6.5 r-patchwork@1.3.2 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://benjburgess.github.io/multiplestressR/
Licenses: GPL 3+
Build system: r
Synopsis: Additive and Multiplicative Null Models for Multiple Stressor Data
Description:

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.

r-mixedlevelrsds 1.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MixedLevelRSDs
Licenses: GPL 2+
Build system: r
Synopsis: Mixed Level Response Surface Designs
Description:

Response Surface Designs (RSDs) involving factors not all at same levels are called Mixed Level RSDs (or Asymmetric RSDs). In many practical situations, RSDs with asymmetric levels will be more suitable as it explores more regions in the design space. (J.S. Mehta and M.N. Das (1968) <doi:10.2307/1267046>. "Asymmetric rotatable designs and orthogonal transformations").This package contains function named ATORDs_I() for generating asymmetric third order rotatable designs (ATORDs) based on third order designs given by Das and Narasimham (1962). Function ATORDs_II() generates asymmetric third order rotatable designs developed using t-design of unequal set sizes, which are smaller in size as compared to design generated by function ATORDs_I(). In general, third order rotatable designs can be classified into two classes viz., designs that are suitable for sequential experimentation and designs for non-sequential experimentation. The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go (M. N. Das and V. Narasimham (1962) <doi:10.1214/AOMS/1177704374>. "Construction of Rotatable Designs through Balanced Incomplete Block Designs"). ATORDs_I() and ATORDs_II() functions generate non-sequential asymmetric third order designs. Function named SeqTORD() generates symmetric sequential third order design in blocks and also gives G-efficiency of the given design. Function named Asymseq() generates asymmetric sequential third order designs in blocks (M. Hemavathi, Eldho Varghese, Shashi Shekhar and Seema Jaggi (2020) <doi:10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)"). In response surface design, situations may arise in which some of the factors are qualitative in nature (Jyoti Divecha and Bharat Tarapara (2017) <doi:10.1080/08982112.2016.1217338>. "Small, balanced, efficient, optimal, and near rotatable response surface designs for factorial experiments asymmetrical in some quantitative, qualitative factors"). The Function named QualRSD() generates second order design with qualitative factors along with their D-efficiency and G-efficiency. The function named RotatabilityQ() calculates a measure of rotatability (measure Q, 0 <= Q <= 1) given by Draper and Pukelshiem(1990) for given a design based on a second order model, (Norman R. Draper and Friedrich Pukelsheim(1990) <doi:10.1080/00401706.1990.10484635>. "Another look at rotatability").

r-multinets 0.2.2
Propagated dependencies: r-rcpp@1.1.0 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/neylsoncrepalde/multinets
Licenses: GPL 3
Build system: r
Synopsis: Multilevel Networks Analysis
Description:

Analyze multilevel networks as described in Lazega et al (2008) <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to igraph'.

r-mobsim 0.3.2
Propagated dependencies: r-vegan@2.7-2 r-sads@0.6.5 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/MoBiodiv/mobsim
Licenses: GPL 3+
Build system: r
Synopsis: Spatial Simulation and Scale-Dependent Analysis of Biodiversity Changes
Description:

Simulation, analysis and sampling of spatial biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017) <doi:10.1111/2041-210x.12986>. In the simulation tools user define the numbers of species and individuals, the species abundance distribution and species aggregation. Functions for analysis include species rarefaction and accumulation curves, species-area relationships and the distance decay of similarity.

r-mlcirtwithin 2.1.2
Propagated dependencies: r-multilcirt@2.12 r-mass@7.3-65 r-limsolve@2.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MLCIRTwithin
Licenses: GPL 2+
Build system: r
Synopsis: Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality
Description:

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.

r-mr-rgm 0.1.0
Propagated dependencies: r-rcppdist@0.1.1.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-igraph@2.2.1 r-gigrvg@0.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/bitansa/MR.RGM
Licenses: GPL 3+
Build system: r
Synopsis: Fitting Multivariate Bidirectional Mendelian Randomization Networks Using Bayesian Directed Cyclic Graphical Models
Description:

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, MR.RGM extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. MR.RGM facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, MR.RGM provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).

r-musicxml 1.0.1
Propagated dependencies: r-xml2@1.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=musicXML
Licenses: GPL 3
Build system: r
Synopsis: Data Sonification using 'musicXML'
Description:

This package provides a set of tools to facilitate data sonification and handle the musicXML format <https://usermanuals.musicxml.com/MusicXML/Content/XS-MusicXML.htm>. Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration. A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the musicXML score, which can then be further processed using specialized music software (e.g. MuseScore', GuitarPro', etc.). Examples can be found in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).

r-mspca 0.2.0
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=msPCA
Licenses: Expat
Build system: r
Synopsis: Sparse Principal Component Analysis with Multiple Principal Components
Description:

This package implements an algorithm for computing multiple sparse principal components of a dataset. The method is based on Cory-Wright and Pauphilet "Sparse PCA with Multiple Principal Components" (2022) <doi:10.48550/arXiv.2209.14790>. The algorithm uses an iterative deflation heuristic with a truncated power method applied at each iteration to compute sparse principal components with controlled sparsity.

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-monan 1.1.0
Propagated dependencies: r-snowfall@1.84-6.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MoNAn
Licenses: GPL 3+
Build system: r
Synopsis: Mobility Network Analysis
Description:

This package implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>. The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks.

r-morph 1.1.0
Propagated dependencies: r-stringr@1.6.0 r-rgl@1.3.31 r-reshape2@1.4.5 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=morph
Licenses: GPL 3
Build system: r
Synopsis: 3D Segmentation of Voxels into Morphologic Classes
Description:

Automatically segments a 3D array of voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been accepted for publication in the journal Landscape Ecology. Detailed references will be updated here once those are known.

r-mmc 0.0.3
Propagated dependencies: r-survival@3.8-3 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mmc
Licenses: GPL 3+
Build system: r
Synopsis: Multivariate Measurement Error Correction
Description:

This package provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

r-mccf1 1.1
Propagated dependencies: r-rocr@1.0-11 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://bitbucket.org/hoffmanlab/mccf1/
Licenses: GPL 2+
Build system: r
Synopsis: Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric and the Best Threshold
Description:

The MCC-F1 analysis is a method to evaluate the performance of binary classifications. The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth. The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.

r-mlz 0.1.5
Propagated dependencies: r-tmb@1.9.18 r-reshape2@1.4.5 r-rcppeigen@0.3.4.0.2 r-gplots@3.2.0 r-ggplot2@4.0.1 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=MLZ
Licenses: GPL 2
Build system: r
Synopsis: Mean Length-Based Estimators of Mortality using TMB
Description:

Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.

r-melt 1.11.4
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-dqrng@0.4.1 r-checkmate@2.3.3 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://docs.ropensci.org/melt/
Licenses: GPL 2+
Build system: r
Synopsis: Multiple Empirical Likelihood Tests
Description:

This package performs multiple empirical likelihood tests. It offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the Eigen C++ library and RcppEigen interface, with OpenMP for parallel computation. Details of the testing procedures are provided in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim, MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

r-mongopipe 0.1.2
Propagated dependencies: r-rlang@1.1.6 r-magrittr@2.0.4 r-jsonlite@2.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://rpkgs.gitlab.io/mongopipe
Licenses: Expat
Build system: r
Synopsis: Write MongoDB Queries with R
Description:

Translate R code into MongoDB aggregation pipelines.

r-mailmerge 0.2.5
Propagated dependencies: r-shiny@1.11.1 r-rstudioapi@0.17.1 r-rmarkdown@2.30 r-purrr@1.2.0 r-miniui@0.1.2 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-googledrive@2.1.2 r-gmailr@3.0.0 r-glue@1.8.0 r-fs@1.6.6 r-commonmark@2.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://andrie.github.io/mailmerge/
Licenses: Expat
Build system: r
Synopsis: Mail Merge Using R Markdown Documents and 'gmailr'
Description:

Perform a mail merge (mass email) using the message defined in markdown, the recipients in a csv file, and gmail as the mailing engine. With this package you can parse markdown documents as the body of email, and the yaml header to specify the subject line of the email. Any braces in the email will be encoded with glue::glue()'. You can preview the email in the RStudio viewer pane, and send (draft) email using gmailr'.

r-mbsts 3.0
Propagated dependencies: r-reshape2@1.4.5 r-pscl@1.5.9 r-mcmcpack@1.7-1 r-matrixstats@1.5.0 r-matrix@1.7-4 r-mass@7.3-65 r-kfas@1.6.0 r-ggplot2@4.0.1 r-bbmisc@1.13
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mbsts
Licenses: LGPL 2.1
Build system: r
Synopsis: Multivariate Bayesian Structural Time Series
Description:

This package provides tools for data analysis with multivariate Bayesian structural time series (MBSTS) models. Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component.

r-mazamaspatialplots 0.3.0
Propagated dependencies: r-tmap@4.2 r-sf@1.0-23 r-rlang@1.1.6 r-mazamaspatialutils@0.8.7 r-mazamacoreutils@0.6.0 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/MazamaScience/MazamaSpatialPlots
Licenses: GPL 3
Build system: r
Synopsis: Thematic Plots for Mazama Spatial Datasets
Description:

This package provides a suite of convenience functions for generating US state and county thematic maps using datasets from the MazamaSpatialUtils package.

r-magclass 6.13.2
Propagated dependencies: r-data-table@1.17.8 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/pik-piam/magclass
Licenses: LGPL 3 FSDG-compatible
Build system: r
Synopsis: Data Class and Tools for Handling Spatial-Temporal Data
Description:

Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).

r-minired 1.0.1
Propagated dependencies: r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=minired
Licenses: GPL 3+
Build system: r
Synopsis: R Interface to 'Redatam' Library
Description:

This package is deprecated. Please use redatamx instead. Provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

r-methcon5 0.1.0
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/EmilHvitfeldt/methcon5
Licenses: Expat
Build system: r
Synopsis: Identify and Rank CpG DNA Methylation Conservation Along the Human Genome
Description:

Identify and rank CpG DNA methylation conservation along the human genome. Specifically it includes bootstrapping methods to provide ranking which should adjust for the differences in length as without it short regions tend to get higher conservation scores.

r-mousetrajectory 0.2.1
Propagated dependencies: r-signal@1.8-1 r-lifecycle@1.0.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mc-schaaf/mousetRajectory
Licenses: GPL 3+
Build system: r
Synopsis: Mouse Trajectory Analyses for Behavioural Scientists
Description:

Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) <doi:10.3758/s13428-020-01409-0>.

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