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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
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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-groupedhyperframe 0.3.4
Propagated dependencies: r-survival@3.8-3 r-spatstat-random@3.4-3 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-spatialpack@0.4-1 r-scales@1.4.0 r-pracma@2.4.6 r-patchwork@1.3.2 r-matrixstats@1.5.0 r-knitr@1.50 r-ggplot2@4.0.1 r-get@1.0-7 r-geomtextpath@0.2.0 r-foreach@1.5.2 r-doparallel@1.0.17 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/tingtingzhan/groupedHyperframe
Licenses: GPL 2
Build system: r
Synopsis: Grouped Hyper Data Frame
Description:

An S3 class groupedHyperframe that inherits from hyper data frame. Batch processes and aggregation of hyper column(s) over a nested grouping structure.

r-gfa 1.0.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GFA
Licenses: Expat
Build system: r
Synopsis: Group Factor Analysis
Description:

Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.

r-genwin 1.0
Propagated dependencies: r-pspline@1.0-21
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GenWin
Licenses: Expat
Build system: r
Synopsis: Spline Based Window Boundaries for Genomic Analyses
Description:

Defines window or bin boundaries for the analysis of genomic data. Boundaries are based on the inflection points of a cubic smoothing spline fitted to the raw data. Along with defining boundaries, a technique to evaluate results obtained from unequally-sized windows is provided. Applications are particularly pertinent for, though not limited to, genome scans for selection based on variability between populations (e.g. using Wright's fixations index, Fst, which measures variability in subpopulations relative to the total population).

r-ggbiplot 0.6.2
Propagated dependencies: r-scales@1.4.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/friendly/ggbiplot
Licenses: GPL 2
Build system: r
Synopsis: Grammar of Graphics Implementation of Biplots
Description:

This package provides a ggplot2 based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using ggplot2 techniques.

r-getmstatistic 0.2.2
Propagated dependencies: r-stargazer@5.2.3 r-psych@2.5.6 r-metafor@4.8-0 r-gtable@0.3.6 r-gridextra@2.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://magosil86.github.io/getmstatistic/
Licenses: Expat
Build system: r
Synopsis: Quantifying Systematic Heterogeneity in Meta-Analysis
Description:

Quantifying systematic heterogeneity in meta-analysis using R. The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical statistical theory, documentation and examples.

r-ggmuller 0.7.0
Propagated dependencies: r-rlang@1.1.6 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/robjohnnoble/ggmuller
Licenses: Expat
Build system: r
Synopsis: Create Muller Plots of Evolutionary Dynamics
Description:

Create plots that combine a phylogeny and frequency dynamics. Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo". Inspired by similar plots in publications of the labs of RE Lenski and JE Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose code this package exploits).

r-ghql 0.1.2
Propagated dependencies: r-r6@2.6.1 r-jsonlite@2.0.0 r-graphql@1.5.3 r-crul@1.6.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://docs.ropensci.org/ghql/
Licenses: Expat
Build system: r
Synopsis: General Purpose 'GraphQL' Client
Description:

This package provides a GraphQL client, with an R6 interface for initializing a connection to a GraphQL instance, and methods for constructing queries, including fragments and parameterized queries. Queries are checked with the libgraphqlparser C++ parser via the graphql package.

r-geocacher 0.1.0
Propagated dependencies: r-tibble@3.3.0 r-threewords@0.1.0 r-stringr@1.6.0 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=geocacheR
Licenses: GPL 3
Build system: r
Synopsis: Tools for Geocaching
Description:

This package provides tools for solving common geocaching puzzle types, and other Geocaching-related tasks.

r-geecure 1.0-6
Propagated dependencies: r-survival@3.8-3 r-matrix@1.7-4 r-mass@7.3-65 r-geepack@1.3.13
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=geecure
Licenses: GPL 2+
Build system: r
Synopsis: Marginal Proportional Hazards Mixture Cure Models with Generalized Estimating Equations
Description:

Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) <doi:10.1016/j.jmva.2013.09.003>.

r-geogenr 2.0.1
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-sf@1.0-23 r-rolap@2.5.2 r-readr@2.1.6 r-httr@1.4.7 r-geomultistar@1.2.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://josesamos.github.io/geogenr/
Licenses: Expat
Build system: r
Synopsis: Generator from American Community Survey Geodatabases
Description:

The American Community Survey (ACS) <https://www.census.gov/programs-surveys/acs> offers geodatabases with geographic information and associated data of interest to researchers in the area. The goal of this package is to generate objects that allow us to access and consult the information available in various formats, such as in GeoPackage format or in multidimensional ROLAP (Relational On-Line Analytical Processing) star format.

r-gepaf 0.2.0
Propagated dependencies: r-bitops@1.0-9
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/riatelab/gepaf
Licenses: GPL 3
Build system: r
Synopsis: Google Encoded Polyline Algorithm Format
Description:

Encode and decode the Google Encoded Polyline Algorithm Format. See <https://developers.google.com/maps/documentation/utilities/polylinealgorithm> for more information.

r-gettz 0.0.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/eddelbuettel/gettz/
Licenses: GPL 2+
Build system: r
Synopsis: Get the Timezone Information
Description:

This package provides a function to retrieve the system timezone on Unix systems which has been found to find an answer when Sys.timezone() has failed. It is based on an answer by Duane McCully posted on StackOverflow', and adapted to be callable from R. The package also builds on Windows, but just returns NULL.

r-gemma2 0.1.3
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/fboehm/gemma2
Licenses: Expat
Build system: r
Synopsis: GEMMA Multivariate Linear Mixed Model
Description:

Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.

r-gabb 0.3.10
Propagated dependencies: r-vegan@2.7-2 r-tidyr@1.3.1 r-pheatmap@1.0.13 r-hotelling@1.0-8 r-ggrepel@0.9.6 r-ggpubr@0.6.2 r-ggplotify@0.1.3 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-egg@0.4.5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GABB
Licenses: Expat
Build system: r
Synopsis: Facilitation of Data Preparation and Plotting Procedures for RDA and PCA Analyses
Description:

Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects. Inputs are : data frame, RDA (package vegan') and PCA (package FactoMineR') objects. Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters.

r-geospt 1.0-6
Propagated dependencies: r-teachingdemos@2.13 r-sp@2.2-0 r-sgeostat@1.0-27 r-plyr@1.8.9 r-minqa@1.2.8 r-mass@7.3-65 r-gstat@2.1-4 r-gsl@2.1-9 r-genalg@0.2.1 r-fields@17.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/amsantac/geospt
Licenses: GPL 2+
Build system: r
Synopsis: Geostatistical Analysis and Design of Optimal Spatial Sampling Networks
Description:

Estimation of the variogram through trimmed mean, radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, pocket plot, and design of optimal sampling networks through sequential and simultaneous points methods.

r-geecrt 1.1.5
Propagated dependencies: r-rootsolve@1.8.2.4 r-mvtnorm@1.3-3 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=geeCRT
Licenses: GPL 2+
Build system: r
Synopsis: Bias-Corrected GEE for Cluster Randomized Trials
Description:

Population-averaged models have been increasingly used in the design and analysis of cluster randomized trials (CRTs). To facilitate the applications of population-averaged models in CRTs, the package implements the generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by Li et al. (2022) <doi:10.1093/biostatistics/kxaa056> specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives a simple and efficient estimating equations approach based on the cluster-period means to estimate the intervention effects as well as correlation parameters. In addition, the package also provides functions for generating correlated binary data with specific mean vector and correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.

r-gamair 1.0-2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gamair
Licenses: GPL 2+
Build system: r
Synopsis: Data for 'GAMs: An Introduction with R'
Description:

Data sets and scripts used in the book Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.

r-glmertree 0.2-6
Propagated dependencies: r-partykit@1.2-24 r-lme4@1.1-37 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmertree
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Generalized Linear Mixed Model Trees
Description:

Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit'. The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; <DOI:10.3758/s13428-024-02389-1>).

r-gagblup 1.0
Propagated dependencies: r-ga@3.2.4 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GAGBLUP
Licenses: GPL 3
Build system: r
Synopsis: Genetic Algorithm Assisted Genomic Best Liner Unbiased Prediction
Description:

This package performs genetic algorithm (Scrucca, L (2013) <doi:10.18637/jss.v053.i04>) assisted genomic best liner unbiased prediction for genomic selection. It also provides a binning method in natural population for genomic selection under the principle of linkage disequilibrium for dimensional reduction.

r-gchartsmap 1.0.1
Propagated dependencies: r-tigris@2.2.1 r-sf@1.0-23 r-jsonlite@2.0.0 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/odeleongt/gchartsmap
Licenses: GPL 3
Build system: r
Synopsis: Access 'Google Charts' Map Data
Description:

Connects to the Google Charts geographic data resources described in <https://developers.google.com/chart/interactive/docs/gallery/geochart>, allowing the user to download contents to use as a reference for related services like Google Trends'.

r-ggdiceplot 1.0.1
Propagated dependencies: r-scales@1.4.0 r-legendry@0.2.4 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/maflot/ggdiceplot
Licenses: Expat
Build system: r
Synopsis: Dice Plot Visualization for 'ggplot2'
Description:

This package provides ggplot2 extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes geom_dice() for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously.

r-gemr 1.2.2
Propagated dependencies: r-scales@1.4.0 r-pracma@2.4.6 r-plsvarsel@0.9.13 r-pls@2.8-5 r-mixlm@1.4.3 r-lme4@1.1-37 r-hdanova@0.8.4 r-gridextra@2.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gemR
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: General Effect Modelling
Description:

Two-step modeling with separation of sources of variation through analysis of variance and subsequent multivariate modeling through a range of unsupervised and supervised statistical methods. Separation can focus on removal of interfering effects or isolation of effects of interest. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.

r-grape 0.1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GRAPE
Licenses: GPL 2
Build system: r
Synopsis: Gene-Ranking Analysis of Pathway Expression
Description:

Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing the consensus behavior of biological pathways in the form of a template, and for quantifying the extent to which individual samples deviate from the template. GRAPE templates are based only on the relative rankings of the genes within the pathway and can be used for classification of tissue types or disease subtypes. GRAPE can be used to represent gene-expression samples as vectors of pathway scores, where each pathway score indicates the departure from a given collection of reference samples. The resulting pathway- space representation can be used as the feature set for various applications, including survival analysis and drug-response prediction. Users of GRAPE should use the following citation: Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics, 18:317 (June 2017).

r-gretel 0.0.1
Propagated dependencies: r-resistorarray@1.0-32 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/davidbuch/gretel
Licenses: GPL 3
Build system: r
Synopsis: Generalized Path Analysis for Social Networks
Description:

The social network literature features numerous methods for assigning value to paths as a function of their ties. gretel systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. More traditional methods are also implemented, as described in Yang, Knoke (2001) <doi:10.1016/S0378-8733(01)00043-0>.

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