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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-gtfsio 1.2.0
Propagated dependencies: r-zip@2.3.3 r-jsonlite@2.0.0 r-fs@1.6.6 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://r-transit.github.io/gtfsio/
Licenses: Expat
Build system: r
Synopsis: Read and Write General Transit Feed Specification (GTFS) Files
Description:

This package provides tools for the development of packages related to General Transit Feed Specification (GTFS) files. Establishes a standard for representing GTFS feeds using R data types. Provides fast and flexible functions to read and write GTFS feeds while sticking to this standard. Defines a basic gtfs class which is meant to be extended by packages that depend on it. And offers utility functions that support checking the structure of GTFS objects.

r-glm-deploy 1.0.4
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/oscarcastrolopez/glm.deploy
Licenses: GPL 3+ FSDG-compatible
Build system: r
Synopsis: 'C' and 'Java' Source Code Generator for Fitted Glm Objects
Description:

This package provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either C or Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated.

r-ggcompare 0.0.6
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://hmu-wh.github.io/ggcompare/
Licenses: Artistic License 2.0
Build system: r
Synopsis: Mean Comparison in 'ggplot2'
Description:

Add mean comparison annotations to a ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).

r-gridgeometry 0.4-0
Propagated dependencies: r-polyclip@1.10-7
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/pmur002/gridgeometry
Licenses: GPL 2+
Build system: r
Synopsis: Polygon Geometry in 'grid'
Description:

This package provides functions for performing polygon geometry with grid grobs. This allows complex shapes to be defined by combining simpler shapes.

r-ghs 0.1
Propagated dependencies: 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=GHS
Licenses: GPL 2
Build system: r
Synopsis: Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler
Description:

Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.

r-gconsensus 0.3.2
Propagated dependencies: r-rjags@4-17 r-mass@7.3-65 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gconsensus
Licenses: GPL 3+
Build system: r
Synopsis: Consensus Value Constructor
Description:

An implementation of the International Bureau of Weights and Measures (BIPM) generalized consensus estimators used to assign the reference value in a key comparison exercise. This can also be applied to any interlaboratory study. Given a set of different sources, primary laboratories or measurement methods this package provides an evaluation of the variance components according to the selected statistical method for consensus building. It also implements the comparison among different consensus builders and evaluates the participating method or sources against the consensus reference value. Based on a diverse set of references, DerSimonian-Laird (1986) <doi:10.1016/0197-2456(86)90046-2>, for a complete list of references look at the reference section in the package documentation.

r-gtrt 0.1.0
Propagated dependencies: r-circular@0.5-2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GTRT
Licenses: GPL 3
Build system: r
Synopsis: Graph Theoretic Randomness Tests
Description:

This package provides a collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) <doi:10.48550/arXiv.2506.21157> and Gehlot and Laha (2025b) <doi:10.48550/arXiv.2506.23522>, respectively.

r-govdown 0.10.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://ukgovdatascience.github.io/govdown/
Licenses: Expat
Build system: r
Synopsis: GOV.UK Style Templates for R Markdown
Description:

This package provides a suite of custom R Markdown formats and templates for authoring web pages styled with the GOV.UK Design System.

r-gseasy 1.5
Propagated dependencies: r-rcpp@1.1.0 r-ontologyindex@2.12
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gsEasy
Licenses: GPL 2+
Build system: r
Synopsis: Gene Set Enrichment Analysis in R
Description:

R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).

r-gpareto 1.1.9
Propagated dependencies: r-rgl@1.3.31 r-rgenoud@5.9-0.11 r-rcpp@1.1.0 r-randtoolbox@2.0.5 r-pso@1.0.4 r-pbivnorm@0.6.0 r-mass@7.3-65 r-ks@1.15.1 r-kriginv@1.4.2 r-emoa@0.5-3 r-dicekriging@1.6.1 r-dicedesign@1.10
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/mbinois/GPareto
Licenses: GPL 3
Build system: r
Synopsis: Gaussian Processes for Pareto Front Estimation and Optimization
Description:

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

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-gsparo 1.0
Propagated dependencies: r-threeway@1.1.4 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=GSparO
Licenses: GPL 2+
Build system: r
Synopsis: Group Sparse Optimization
Description:

Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_p,q regularization. Journal of Machine Learning Research, to appear, 2017".

r-gametheory 2.7.1
Propagated dependencies: r-lpsolveapi@5.5.2.0-17.14 r-kappalab@0.4-12 r-ineq@0.2-13 r-gtools@3.9.5 r-combinat@0.0-8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GameTheory
Licenses: GPL 2+
Build system: r
Synopsis: Cooperative Game Theory
Description:

Implementation of a common set of punctual solutions for Cooperative Game Theory.

r-gse 4.2-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GSE
Licenses: GPL 2+
Build system: r
Synopsis: Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data
Description:

Robust Estimation of Multivariate Location and Scatter in the Presence of Cellwise and Casewise Contamination and Missing Data.

r-gsstda 1.0.0
Propagated dependencies: r-visnetwork@2.1.4 r-survival@3.8-3 r-devtools@2.4.6 r-complexheatmap@2.26.0 r-cluster@2.1.8.1 r-circlize@0.4.16
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GSSTDA
Licenses: GPL 3
Build system: r
Synopsis: Progression Analysis of Disease with Survival using Topological Data Analysis
Description:

Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" <doi:10.1101/2022.09.08.507080>.

r-grafify 5.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/ashenoy-cmbi/grafify
Licenses: GPL 2+
Build system: r
Synopsis: Easy Graphs for Data Visualisation and Linear Models for ANOVA
Description:

Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.

r-gdim 0.1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/RoheLab/gdim
Licenses: GPL 3+
Build system: r
Synopsis: Estimate Graph Dimension using Cross-Validated Eigenvalues
Description:

Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. The training graph is used to estimate eigenvectors, and the test graph is used to evaluate the correlation between the training eigenvectors and the eigenvectors of the test graph. The correlations follow a simple central limit theorem that can be used to estimate graph dimension via hypothesis testing, see Chen et al. (2021) <doi:10.48550/arXiv.2108.03336> for details.

r-ggswissmaps 0.1.2
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/gibonet/ggswissmaps
Licenses: GPL 2
Build system: r
Synopsis: Offers Various Swiss Maps as Data Frames and 'ggplot2' Objects
Description:

Offers various swiss maps as data frames and ggplot2 objects and gives the possibility to add layers of data on the maps. Data are publicly available from the swiss federal statistical office. In addition to the \codemaps2 object (a list of 8 swiss maps, at various levels), there are the data frames with the boundaries used to produce these maps (\codeshp_df, a list with 8 data frames).

r-genpathmox 1.1
Propagated dependencies: r-matrixcalc@1.0-6 r-diagram@1.6.5 r-csem@0.6.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=genpathmox
Licenses: GPL 3
Build system: r
Synopsis: Pathmox Approach Segmentation Tree Analysis
Description:

It provides an interesting solution for handling a high number of segmentation variables in partial least squares structural equation modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) to detect the path coefficients responsible for the identified differences). The package also allows running the hybrid multi-group approach (Lamberti (2021) <doi:10.1007/s11135-021-01096-9>).

r-growthmodels 1.3.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/drodriguezperez/growthmodels
Licenses: GPL 3
Build system: r
Synopsis: Nonlinear Growth Models
Description:

This package provides a compilation of nonlinear growth models.

r-ggseqplot 0.8.9
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://maraab23.github.io/ggseqplot/
Licenses: GPL 3+
Build system: r
Synopsis: Render Sequence Plots using 'ggplot2'
Description:

This package provides a set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas TraMineR uses base R to produce the plots this library draws on ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class ggplot', i.e. components can be added and tweaked using + and regular ggplot2 functions.

r-graphicalextremes 0.3.4
Propagated dependencies: r-rdpack@2.6.4 r-osqp@0.6.3.3 r-mvtnorm@1.3-3 r-igraph@2.2.1 r-glmnet@4.1-10 r-glassofast@1.0.1 r-cvxr@1.0-15 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/sebastian-engelke/graphicalExtremes
Licenses: GPL 3
Build system: r
Synopsis: Statistical Methodology for Graphical Extreme Value Models
Description:

Statistical methodology for sparse multivariate extreme value models. Methods are provided for exact simulation and statistical inference for multivariate Pareto distributions on graphical structures as described in the paper Graphical Models for Extremes by Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.

r-ginsarcorw 1.15.8
Propagated dependencies: r-sp@2.2-0 r-raster@3.6-32 r-circular@0.5-2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: <https://subhadipdatta.wixsite.com/profile/post/ginsarcorw-gacos-insar-correction-workflow>
Licenses: GPL 3
Build system: r
Synopsis: GACOS InSAR Correction Workflow
Description:

This package provides a workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework. References: Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.

r-gseries 3.0.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://StatCan.github.io/gensol-gseries/en/
Licenses: GPL 3+
Build system: r
Synopsis: Improve the Coherence of Your Time Series Data
Description:

R version of G-Series', Statistics Canada's generalized system devoted to the benchmarking and reconciliation of time series data. The methods used in G-Series essentially come from Dagum, E. B., and P. Cholette (2006) <doi:10.1007/0-387-35439-5>.

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