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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-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-gofcens 1.5
Propagated dependencies: r-survival@3.8-3 r-gridextra@2.3 r-ggplot2@4.0.1 r-fitdistrplus@1.2-4 r-boot@1.3-32 r-actuar@3.3-6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://arnaugarciagrbio.github.io/GofCens/
Licenses: GPL 2+
Build system: r
Synopsis: Goodness-of-Fit Methods for Right-Censored Data
Description:

Graphical tools and goodness-of-fit tests for right-censored data: 1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which use the empirical distribution function for complete data and are extended for right-censored data. 2. Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data. 3. A series of graphical tools such as probability or cumulative hazard plots to guide the decision about the most suitable parametric model for the data. These functions share several features as they can handle both complete and right-censored data, and they provide parameter estimates for the distributions under study.

r-gdpc 1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gdpc
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Dynamic Principal Components
Description:

This package provides functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02> for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy.

r-gmwmx 1.0.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gmwmx
Licenses: AGPL 3
Build system: r
Synopsis: Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals
Description:

This package implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>. The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals. The gmwmx package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.

r-glam 1.0.2
Propagated dependencies: r-gam@1.22-6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glam
Licenses: Expat
Build system: r
Synopsis: Generalized Additive and Linear Models (GLAM)
Description:

This package contains methods for fitting Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). Generalized regression models are common methods for handling data for which assuming Gaussian-distributed errors is not appropriate. For instance, if the response of interest is binary, count, or proportion data, one can instead model the expectation of the response based on an appropriate data-generating distribution. This package provides methods for fitting GLMs and GAMs under Beta regression, Poisson regression, Gamma regression, and Binomial regression (currently GLM only) settings. Models are fit using local scoring algorithms described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.

r-googlecloudvisionr 0.2.0
Propagated dependencies: r-purrr@1.2.0 r-jsonlite@2.0.0 r-googleauthr@2.0.2.1 r-glue@1.8.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=googleCloudVisionR
Licenses: Expat
Build system: r
Synopsis: Access to the 'Google Cloud Vision' API for Image Recognition, OCR and Labeling
Description:

Interact with the Google Cloud Vision <https://cloud.google.com/vision/> API in R. Part of the cloudyr <https://cloudyr.github.io/> project.

r-guaguas 0.3.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/rivaquiroga/guaguas
Licenses: CC0
Build system: r
Synopsis: Nombres Inscritos en Chile (1920 - 2021)
Description:

Datos de nombres inscritos en Chile entre 1920 y 2021, de acuerdo al Servicio de Registro Civil. English: Chilean baby names registered from 1920 to 2021 by the Civil Registry Service.

r-gkwdist 1.1.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/evandeilton/gkwdist
Licenses: Expat
Build system: r
Synopsis: Generalized Kumaraswamy Distribution Family
Description:

This package implements the five-parameter Generalized Kumaraswamy ('gkw') distribution proposed by Carrasco, Ferrari and Cordeiro (2010) <doi:10.48550/arXiv.1004.0911> and its seven nested sub-families for modeling bounded continuous data on the unit interval (0,1). The gkw distribution extends the Kumaraswamy distribution described by Jones (2009) <doi:10.1016/j.stamet.2008.04.001>. Provides density, distribution, quantile, and random generation functions, along with analytical log-likelihood, gradient, and Hessian functions implemented in C++ via RcppArmadillo for maximum computational efficiency. Suitable for modeling proportions, rates, percentages, and indices exhibiting complex features such as asymmetry, or heavy tails and other shapes not adequately captured by standard distributions like simple Beta or Kumaraswamy.

r-ggiraphextra 0.3.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/cardiomoon/ggiraphExtra
Licenses: GPL 3
Build system: r
Synopsis: Make Interactive 'ggplot2'. Extension to 'ggplot2' and 'ggiraph'
Description:

Collection of functions to enhance ggplot2 and ggiraph'. Provides functions for exploratory plots. All plot can be a static plot or an interactive plot using ggiraph'.

r-gridonclusters 0.3.2
Propagated dependencies: r-rdpack@2.6.4 r-rcpp@1.1.0 r-plotrix@3.8-13 r-mclust@6.1.2 r-fossil@0.4.0 r-dqrng@0.4.1 r-cluster@2.1.8.1 r-ckmeans-1d-dp@4.3.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GridOnClusters
Licenses: LGPL 3+
Build system: r
Synopsis: Multivariate Joint Grid Discretization
Description:

Discretize multivariate continuous data using a grid to capture the joint distribution that preserves clusters in original data. It can handle both labeled or unlabeled data. Both published methods (Wang et al 2020) <doi:10.1145/3388440.3412415> and new methods are included. Joint grid discretization can prepare data for model-free inference of association, function, or causality.

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-gaawr2 0.0.5
Propagated dependencies: r-survival@3.8-3 r-rdpack@2.6.4 r-ggplot2@4.0.1 r-gap-datasets@0.0.6 r-gap@1.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://jinghuazhao.github.io/gaawr2/
Licenses: Expat
Build system: r
Synopsis: Genetic Association Analysis
Description:

This is a companion to Henry-Stewart talk by Zhao (2026, <doi:10.69645/FRFQ9519>), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, <doi:10.69645/DCRY5578>) provides an brief introduction to these topics.

r-globalopttests 1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=globalOptTests
Licenses: GPL 3+
Build system: r
Synopsis: Objective functions for benchmarking the performance of global optimization algorithms
Description:

This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms.

r-goodmankruskal 0.0.3
Propagated dependencies: r-corrplot@0.95 r-classint@0.4-11
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GoodmanKruskal
Licenses: Expat
Build system: r
Synopsis: Association Analysis for Categorical Variables
Description:

Association analysis between categorical variables using the Goodman and Kruskal tau measure. This asymmetric association measure allows the detection of asymmetric relations between categorical variables (e.g., one variable obtained by re-grouping another).

r-grpslope 0.3.4
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/agisga/grpSLOPE
Licenses: GPL 3
Build system: r
Synopsis: Group Sorted L1 Penalized Estimation
Description:

Group SLOPE (Group Sorted L1 Penalized Estimation) is a penalized linear regression method that is used for adaptive selection of groups of significant predictors in a high-dimensional linear model. The Group SLOPE method can control the (group) false discovery rate at a user-specified level (i.e., control the expected proportion of irrelevant among all selected groups of predictors). For additional information about the implemented methods please see Brzyski, Gossmann, Su, Bogdan (2018) <doi:10.1080/01621459.2017.1411269>.

r-ginax 0.1.0
Propagated dependencies: r-memoise@2.0.1 r-matrix@1.7-4 r-ga@3.2.4 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GINAX
Licenses: GPL 3
Build system: r
Synopsis: Performs Genome-Wide Iterative Fine-Mapping for Non-Gaussian Data using GINA-X
Description:

This package implements GINA-X, a genome-wide iterative fine-mapping method designed for non-Gaussian traits. It supports the identification of credible sets of genetic variants.

r-gangenerativedata 2.1.6
Propagated dependencies: r-tensorflow@2.20.0 r-rcpp@1.1.0 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=ganGenerativeData
Licenses: GPL 2+
Build system: r
Synopsis: Generate Generative Data for a Data Source
Description:

Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.

r-ggip 0.3.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://davidchall.github.io/ggip/
Licenses: Expat
Build system: r
Synopsis: Data Visualization for IP Addresses and Networks
Description:

This package provides a ggplot2 extension that enables visualization of IP (Internet Protocol) addresses and networks. The address space is mapped onto the Cartesian coordinate system using a space-filling curve. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces.

r-ggnormalviolin 0.2.1
Propagated dependencies: r-scales@1.4.0 r-magrittr@2.0.4 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/wjschne/ggnormalviolin
Licenses: CC0
Build system: r
Synopsis: 'ggplot2' Extension to Make Normal Violin Plots
Description:

Uses ggplot2 to create normally distributed violin plots with specified means and standard deviations. This function can be useful in showing hypothetically normal distributions and confidence intervals.

r-gjls2 0.2.0
Propagated dependencies: r-quantreg@6.1 r-plyr@1.8.9 r-nlme@3.1-168 r-moments@0.14.1 r-mcmcpack@1.7-1 r-mass@7.3-65 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=gJLS2
Licenses: GPL 3+
Build system: r
Synopsis: Generalized Joint Location and Scale Framework for Association Testing
Description:

An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).

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-gawdis 0.1.5
Propagated dependencies: r-ga@3.2.4 r-fd@1.0-12.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/pavel-fibich/gawdis/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Multi-Trait Dissimilarity with more Uniform Contributions
Description:

R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.

r-gcat 0.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gCat
Licenses: GPL 2+
Build system: r
Synopsis: Graph-Based Two-Sample Tests for Categorical Data
Description:

These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories.

r-glmmselect 1.2.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GLMMselect
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Model Selection for Generalized Linear Mixed Models
Description:

This package provides a Bayesian model selection approach for generalized linear mixed models. Currently, GLMMselect can be used for Poisson GLMM and Bernoulli GLMM. GLMMselect can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. GLMMselect can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. GLMMselect is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.

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