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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-gifi 1.0-0
Propagated dependencies: r-colorspace@2.1-2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://r-forge.r-project.org/projects/psychor/
Licenses: GPL 3
Build system: r
Synopsis: Multivariate Analysis with Optimal Scaling
Description:

This package implements various Gifi methods in a user-friendly way: categorical principal component analysis (princals), multiple correspondence analysis (homals), monotone regression analysis (morals).

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-gmvjoint 0.4.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/jamesmurray7/gmvjoint
Licenses: GPL 3
Build system: r
Synopsis: Joint Models of Survival and Multivariate Longitudinal Data
Description:

Fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models. The joint models are fit via maximum likelihood using an approximate expectation maximisation algorithm. Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.

r-geotox 0.3.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://niehs.github.io/GeoTox/
Licenses: Expat
Build system: r
Synopsis: Spatiotemporal Mixture Risk Assessment
Description:

Connecting spatiotemporal exposure to individual and population-level risk via source-to-outcome continuum modeling. The package, methods, and case-studies are described in Messier, Reif, and Marvel (2025) <doi:10.1186/s40246-024-00711-8> and Eccles et al. (2023) <doi:10.1016/j.scitotenv.2022.158905>.

r-genderstat 0.1.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=genderstat
Licenses: Expat
Build system: r
Synopsis: Quantitative Analysis Tools for Gender Studies
Description:

This package provides tools for quantitative analysis in gender studies, including functions to calculate various gender inequality metrics such as the Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI), and Gender Empowerment Measure (GEM). Also includes extracted secondary example datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center and the World Bank Gender Data Portal by the author the dataset is available on <doi:10.34740/kaggle/dsv/6359326>. References: Miller, Kevin; Vagins, Deborah J. (2021) <https://eric.ed.gov/?id=ED596219>. Jacques Charmes & Saskia Wieringa (2003) <doi:10.1080/1464988032000125773>. Gaëlle Ferrant (2010) <https://shs.hal.science/halshs-00462463/>.

r-genest 1.4.9
Propagated dependencies: r-survival@3.8-3 r-shinyjs@2.1.0 r-shiny@1.11.1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-matrixstats@1.5.0 r-mass@7.3-65 r-lubridate@1.9.4 r-htmltools@0.5.8.1 r-hellno@0.0.1 r-gtools@3.9.5 r-gsl@2.1-9 r-dt@0.34.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GenEst
Licenses: CC0
Build system: r
Synopsis: Generalized Mortality Estimator
Description:

Command-line and shiny GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.

r-gift 1.3.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/BioGeoMacro/GIFT
Licenses: GPL 2+
Build system: r
Synopsis: Access to the Global Inventory of Floras and Traits (GIFT)
Description:

Retrieving regional plant checklists, species traits and distributions, and environmental data from the Global Inventory of Floras and Traits (GIFT). More information about the GIFT database can be found at <https://gift.uni-goettingen.de/about> and the map of available floras can be visualized at <https://gift.uni-goettingen.de/map>. The API and associated queries can be accessed according the following scheme: <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.

r-ggdibbler 0.6.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://harriet-mason.github.io/ggdibbler/
Licenses: GPL 3
Build system: r
Synopsis: Add Uncertainty to Data Visualisations
Description:

This package provides a ggplot2 extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas ggdibbler differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart.

r-graphicalevidence 1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=graphicalEvidence
Licenses: GPL 3
Build system: r
Synopsis: Graphical Evidence
Description:

Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. The top level function used to estimate marginal likelihood is called evidence(), which expects the prior name, data, and relevant prior specific parameters. This package also provides an MCMC prior sampler using the same underlying approach, implemented in prior_sampling(), which expects a prior name and prior specific parameters. Both functions also expect the number of burn-in iterations and the number of sampling iterations for the underlying MCMC sampler.

r-gctensor 1.0.0
Propagated dependencies: r-rtensor@1.4.9 r-einsum@0.1.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/rikenbit/gcTensor
Licenses: Expat
Build system: r
Synopsis: Generalized Coupled Tensor Factorization
Description:

Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md <https://github.com/rikenbit/gcTensor>, for details of the method.

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-ggfishplots 0.4.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://deepwaterimr.github.io/ggFishPlots/
Licenses: GPL 3
Build system: r
Synopsis: Visualise and Calculate Life History Parameters for Fisheries Science using 'ggplot2'
Description:

This package contains functions to create life history parameter plots from raw data. The plots are created using ggplot2', and calculations done using the tidyverse collection of packages. The package contains references to FishBase (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.

r-glmpath 0.98
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmpath
Licenses: GPL 2+
Build system: r
Synopsis: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
Description:

This package provides a path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.

r-gfiultra 1.0.0
Propagated dependencies: r-sis@0.8-8 r-mvtnorm@1.3-3 r-lazyeval@0.2.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/stla/gfiUltra
Licenses: GPL 3
Build system: r
Synopsis: Generalized Fiducial Inference for Ultrahigh-Dimensional Regression
Description:

Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.

r-gater 0.1.16
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/lance-waller-lab/gateR
Licenses: ASL 2.0
Build system: r
Synopsis: Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation
Description:

Estimates statistically significant marker combination values within which one immunologically distinctive group (i.e., disease case) is more associated than another group (i.e., healthy control), successively, using various combinations (i.e., "gates") of markers to examine features of cells that may be different between groups. For a two-group comparison, the gateR package uses the spatial relative risk function estimated using the sparr package. Details about the sparr package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.

r-glober 1.0
Propagated dependencies: r-plot3d@1.4.2 r-matrix@1.7-4 r-ggplot2@4.0.1 r-genlasso@1.6.1 r-fda@6.3.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glober
Licenses: GPL 2
Build system: r
Synopsis: Estimating Functions with Multivariate B-Splines
Description:

Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.

r-ggfigdone 0.1.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=ggfigdone
Licenses: GPL 3
Build system: r
Synopsis: Manage & Modify 'ggplot' Figures using 'ggfigdone'
Description:

When you prepare a presentation or a report, you often need to manage a large number of ggplot figures. You need to change the figure size, modify the title, label, themes, etc. It is inconvenient to go back to the original code to make these changes. This package provides a simple way to manage ggplot figures. You can easily add the figure to the database and update them later using CLI (command line interface) or GUI (graphical user interface).

r-giraf 1.0.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GiRaF
Licenses: GPL 2+
Build system: r
Synopsis: Gibbs Random Fields Analysis
Description:

Allows calculation on, and sampling from Gibbs Random Fields, and more precisely general homogeneous Potts model. The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices.

r-gofcopula 0.4-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gofCopula
Licenses: GPL 3+
Build system: r
Synopsis: Goodness-of-Fit Tests for Copulae
Description:

Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.

r-gpvecchia 0.1.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GPvecchia
Licenses: GPL 2+
Build system: r
Synopsis: Scalable Gaussian-Process Approximations
Description:

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <doi:10.48550/arXiv.1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <doi:10.48550/arXiv.1706.02205> and MaxMin ordering proposed in Guinness (2018) <doi:10.48550/arXiv.1609.05372>.

r-glmfitmiss 2.1.0
Propagated dependencies: r-mass@7.3-65 r-dplyr@1.1.4 r-data-table@1.17.8 r-brglm2@1.0.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmfitmiss
Licenses: Expat
Build system: r
Synopsis: Fitting GLMs with Missing Data in Both Responses and Covariates
Description:

Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firthâ s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) <https:>, Maiti and Pradhan (2009) <doi:10.1111/j.1541-0420.2008.01186.x>, Maity, Pradhan, and Das (2019) <doi:10.1080/00031305.2017.1407359> for further methodological details.

r-gfdrmst 0.1.1
Propagated dependencies: r-tippy@0.1.0 r-shinywidgets@0.9.0 r-shinythemes@1.2.0 r-shinymatrix@0.8.1 r-shinyjs@2.1.0 r-shiny@1.11.1 r-plyr@1.8.9 r-mvtnorm@1.3-3 r-mass@7.3-65 r-lpsolve@5.6.23 r-gfdmcv@0.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GFDrmst
Licenses: GPL 3+
Build system: r
Synopsis: Multiple RMST-Based Tests in General Factorial Designs
Description:

We implemented multiple tests based on the restricted mean survival time (RMST) for general factorial designs as described in Munko et al. (2024) <doi:10.1002/sim.10017>. Therefore, an asymptotic test, a groupwise bootstrap test, and a permutation test are incorporated with a Wald-type test statistic. The asymptotic and groupwise bootstrap test take the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMST contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.

r-gmapsdistance 4.0.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/jlacko/gmapsdistance
Licenses: GPL 3+
Build system: r
Synopsis: Distance and Travel Time Between Two Points from Google Maps
Description:

Get distance and travel time between two points from Google Maps. Four possible modes of transportation (bicycling, walking, driving and public transportation).

r-gbs2ploidy 1.0
Propagated dependencies: r-rjags@4-17 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=gbs2ploidy
Licenses: GPL 3
Build system: r
Synopsis: Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data
Description:

This package provides functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.

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Total results: 69112