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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-goft 1.3.6
Propagated dependencies: r-sn@2.1.1 r-fitdistrplus@1.2-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=goft
Licenses: GPL 3+
Build system: r
Synopsis: Tests of Fit for some Probability Distributions
Description:

Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, Weibull', Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.

r-genescape 1.0
Propagated dependencies: r-mass@7.3-65 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GeneScape
Licenses: GPL 3+
Build system: r
Synopsis: Simulation of Single Cell RNA-Seq Data with Complex Structure
Description:

Simulating single cell RNA-seq data with complicated structure. This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) <doi:10.1186/s13059-017-1305-0>). GeneScape incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes.

r-gcpm 1.2.2
Propagated dependencies: r-rcppprogress@0.4.2 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GCPM
Licenses: GPL 2
Build system: r
Synopsis: Generalized Credit Portfolio Model
Description:

Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities.

r-gpcsign 0.1.1
Propagated dependencies: r-truncatednormal@2.3 r-tmvtnorm@1.7 r-future-apply@1.20.0 r-future@1.68.0 r-dicekriging@1.6.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GPCsign
Licenses: GPL 3
Build system: r
Synopsis: Gaussian Process Classification as Described in Bachoc et al. (2020)
Description:

Parameter estimation and prediction of Gaussian Process Classifier models as described in Bachoc et al. (2020) <doi:10.1007/S10898-020-00920-0>. Important functions : gpcm(), predict.gpcm(), update.gpcm().

r-gdm 1.6.0-7
Propagated dependencies: r-vegan@2.7-2 r-reshape2@1.4.5 r-rcpp@1.1.0 r-pbapply@1.7-4 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://mfitzpatrick.al.umces.edu/gdm/
Licenses: GPL 3+
Build system: r
Synopsis: Generalized Dissimilarity Modeling
Description:

This package provides a toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) <doi:10.1111/geb.13459> Ferrier S, Manion G, Elith J, Richardson K (2007) <doi:10.1111/j.1472-4642.2007.00341.x>.

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-gjam 2.7
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rann@2.6.2 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=gjam
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Joint Attribute Modeling
Description:

Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.

r-genieclust 1.2.0
Propagated dependencies: r-rcpp@1.1.0 r-quitefastmst@0.9.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://genieclust.gagolewski.com/
Licenses: AGPL 3
Build system: r
Synopsis: Fast and Robust Hierarchical Clustering with Noise Point Detection
Description:

The Genie algorithm (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) is a robust and outlier-resistant hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). This package features its faster and more powerful version. It allows clustering with respect to mutual reachability distances, enabling it to act as a noise point detector or a version of HDBSCAN* that can identify a predefined number of clusters. The package also features an implementation of the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The Python version of genieclust is available via PyPI'.

r-griddebug 0.5-1
Dependencies: graphviz@7.0.1
Propagated dependencies: r-gridsvg@1.7-7 r-gridgraphviz@0.3-2 r-graph@1.88.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gridDebug
Licenses: GPL 2+
Build system: r
Synopsis: Debugging 'grid' Graphics
Description:

This package provides functions for drawing scene trees representing scenes that have been drawn using grid graphics.

r-genhmm1d 0.2.6
Propagated dependencies: r-vgam@1.1-13 r-vares@1.0.2 r-stabledist@0.7-2 r-ssdtools@2.5.0 r-sn@2.1.1 r-skewt@1.0 r-sgt@2.0 r-rmutil@1.1.10 r-reshape2@1.4.5 r-matrixcalc@1.0-6 r-gldex@2.0.0.9.4 r-ggplot2@4.0.1 r-generalizedhyperbolic@0.8-7 r-gamlss-dist@6.1-1 r-foreach@1.5.2 r-extradistr@1.10.0 r-envstats@3.1.0 r-doparallel@1.0.17 r-actuar@3.3-6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GenHMM1d
Licenses: GPL 3
Build system: r
Synopsis: Goodness-of-Fit for Zero-Inflated Univariate Hidden Markov Models
Description:

Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate Hidden Markov Models (HMM), including zero-inflated distributions. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Nasri et al (2020) <doi:10.1029/2019WR025122>.

r-gitgadget 0.8.2
Propagated dependencies: r-usethis@3.2.1 r-shinyfiles@0.9.3 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-miniui@0.1.2 r-markdown@2.0 r-jsonlite@2.0.0 r-dplyr@1.1.4 r-curl@7.0.0 r-callr@3.7.6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/vnijs/gitgadget
Licenses: GPL 3
Build system: r
Synopsis: 'Rstudio' Addin for Version Control and Assignment Management using Git
Description:

An Rstudio addin for version control that allows users to clone repositories, create and delete branches, and sync forks on GitHub, GitLab, etc. Furthermore, the addin uses the GitLab API to allow instructors to create forks and merge requests for all students/teams with one click of a button.

r-gena 1.0.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gena
Licenses: GPL 2+
Build system: r
Synopsis: Genetic Algorithm and Particle Swarm Optimization
Description:

This package implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.archives-ouvertes.fr/hal-00764996>.

r-getbcbdata 0.9.1
Propagated dependencies: r-purrr@1.2.0 r-parallelly@1.45.1 r-memoise@2.0.1 r-jsonlite@2.0.0 r-future@1.68.0 r-furrr@0.3.1 r-dplyr@1.1.4 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/msperlin/GetBCBData/
Licenses: GPL 2
Build system: r
Synopsis: Imports Datasets from BCB (Central Bank of Brazil) using Its Official API
Description:

Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the memoise package and , multicore/multisession with furrr and format of output data (long/wide).

r-gwsdat 3.3.0
Propagated dependencies: r-zoo@1.8-14 r-splancs@2.01-45 r-sp@2.2-0 r-sm@2.2-6.0 r-shinyjs@2.1.0 r-shinydashboard@0.7.3 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-sf@1.0-23 r-rhandsontable@0.3.8 r-readxl@1.4.5 r-raster@3.6-32 r-officer@0.7.1 r-matrix@1.7-4 r-mass@7.3-65 r-lubridate@1.9.4 r-lattice@0.22-7 r-kendall@2.2.1 r-geometry@0.5.2 r-digest@0.6.39 r-deldir@2.0-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GWSDAT
Licenses: GPL 3
Build system: r
Synopsis: GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)
Description:

Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.

r-groupcompare 1.0.1
Propagated dependencies: r-vioplot@0.5.1 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=groupcompare
Licenses: GPL 2+
Build system: r
Synopsis: Comparing Two Groups Using Various Descriptive Statistics
Description:

Comparing two independent or paired groups across a range of descriptive statistics, enabling the evaluation of potential differences in central tendency (mean, median), dispersion (variance, interquartile range), shape (skewness, kurtosis), and distributional characteristics (various quantiles). The analytical framework incorporates parametric t-tests, non-parametric Wilcoxon tests, permutation tests, and bootstrap resampling techniques to assess the statistical significance of observed differences.

r-gsse 0.1
Propagated dependencies: r-zoo@1.8-14 r-iso@0.0-21
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GSSE
Licenses: GPL 2+
Build system: r
Synopsis: Genotype-Specific Survival Estimation
Description:

We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees. We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function. The estimators are calculated via an expectation-maximization algorithm.

r-glmmsel 1.0.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/ryan-thompson/glmmsel
Licenses: GPL 3
Build system: r
Synopsis: Generalised Linear Mixed Model Selection
Description:

This package provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Thompson, Wand, and Wang (2025) <doi:10.48550/arXiv.2506.20425>.

r-gm 2.0.0
Propagated dependencies: r-htmltools@0.5.8.1 r-erify@0.6.0 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/flujoo/gm
Licenses: Expat
Build system: r
Synopsis: Create Music with Ease
Description:

This package provides a simple and intuitive high-level language for music representation. Generates and embeds music scores and audio files in RStudio', R Markdown documents, and R Jupyter Notebooks'. Internally, uses MusicXML <https://github.com/w3c/musicxml> to represent music, and MuseScore <https://musescore.org/> to convert MusicXML'.

r-gmdatabase 0.5.1
Propagated dependencies: r-shiny@1.11.1 r-rmysql@0.11.1 r-foreach@1.5.2 r-digest@0.6.39 r-dbi@1.2.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gmDatabase
Licenses: GPL 2+ LGPL 2.0+
Build system: r
Synopsis: Accessing a Geometallurgical Database with R
Description:

This package provides a template for a geometallurgical database and a fast and easy interface for accessing it.

r-gagas 0.6.2
Propagated dependencies: r-survival@3.8-3 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://arxiv.org/abs/1911.00658
Licenses: GPL 2
Build system: r
Synopsis: Global Adaptive Generative Adjustment Algorithm for Generalized Linear Models
Description:

Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076).

r-ggstudent 0.1.2
Propagated dependencies: 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/helske/ggstudent
Licenses: GPL 2+
Build system: r
Synopsis: Continuous Confidence Interval Plots using t-Distribution
Description:

This package provides an extension to ggplot2 (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean. These plots contain multiple user-defined confidence areas with varying colours, defined by the underlying t-distribution used to compute standard confidence intervals for the mean of the normal distribution when the variance is unknown. Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution. These visualizations are studied in (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) <doi:10.1109/TVCG.2021.3073466>.

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-gpbayes 0.1.0-6
Dependencies: gsl@2.8
Propagated dependencies: r-rcppprogress@0.4.2 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GPBayes
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Gaussian Process Modeling in Uncertainty Quantification
Description:

Gaussian processes ('GPs') have been widely used to model spatial data, spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on GPs with spatial data, spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the Matérn family and the Confluent Hypergeometric family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating GPs with all the implemented covariance functions; (5) unified implementation to allow easy specification of various GPs'.

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