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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-guidedpls 1.1.0
Propagated dependencies: r-irlba@2.3.5.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/rikenbit/guidedPLS
Licenses: Expat
Build system: r
Synopsis: Supervised Dimensional Reduction by Guided Partial Least Squares
Description:

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

r-grouper 0.5.0
Propagated dependencies: r-yaml@2.3.10 r-rlang@1.1.6 r-ompr@1.0.4 r-magrittr@2.0.4 r-dplyr@1.1.4 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=grouper
Licenses: Expat
Build system: r
Synopsis: Optimal Assignment of Students to Groups
Description:

Integer programming models to assign students to groups by maximising diversity within groups, or by maximising preference scores for topics.

r-ggfootball 0.3.0
Propagated dependencies: r-tibble@3.3.0 r-jsonlite@2.0.0 r-httr@1.4.7 r-highcharter@0.9.4 r-glue@1.8.0 r-ggsoccer@0.2.0 r-ggplot2@4.0.1 r-ggiraph@0.9.2 r-gfonts@0.2.0 r-gdtools@0.4.4 r-dplyr@1.1.4 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://aymennasri.github.io/ggfootball/
Licenses: GPL 3+
Build system: r
Synopsis: Plotting Football Matches Expected Goals (xG) Stats with 'Understat' Data
Description:

Scrapes football match shots data from Understat <https://understat.com/> and visualizes it using interactive plots: - A detailed shot map displaying the location, type, and xG value of shots taken by both teams. - An xG timeline chart showing the cumulative xG for each team over time, annotated with the details of scored goals.

r-gapclosing 1.0.2
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-ranger@0.17.0 r-mgcv@1.9-4 r-magrittr@2.0.4 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-foreach@1.5.2 r-forcats@1.0.1 r-dplyr@1.1.4 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://ilundberg.github.io/gapclosing/
Licenses: Expat
Build system: r
Synopsis: Estimate Gaps Under an Intervention
Description:

This package provides functions to estimate the disparities across categories (e.g. Black and white) that persists if a treatment variable (e.g. college) is equalized. Makes estimates by treatment modeling, outcome modeling, and doubly-robust augmented inverse probability weighting estimation, with standard errors calculated by a nonparametric bootstrap. Cross-fitting is supported. Survey weights are supported for point estimation but not for standard error estimation; those applying this package with complex survey samples should consult the data distributor to select an appropriate approach for standard error construction, which may involve calling the functions repeatedly for many sets of replicate weights provided by the data distributor. The methods in this package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.

r-gggenomes 1.1.2
Propagated dependencies: r-vctrs@0.6.5 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-snakecase@0.11.1 r-scales@1.4.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-colorspace@2.1-2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://thackl.github.io/gggenomes/
Licenses: Expat
Build system: r
Synopsis: Grammar of Graphics for Comparative Genomics
Description:

An extension of ggplot2 for creating complex genomic maps. It builds on the power of ggplot2 and tidyverse adding new ggplot2'-style geoms & positions and dplyr'-style verbs to manipulate the underlying data. It implements a layout concept inspired by ggraph and introduces tracks to bring tidiness to the mess that is genomics data.

r-gqlr 0.1.0
Propagated dependencies: r-r6@2.6.1 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-graphql@1.5.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: http://schloerke.com/gqlr/
Licenses: Expat
Build system: r
Synopsis: 'GraphQL' Server in R
Description:

Server implementation of GraphQL <http://spec.graphql.org/>, a query language originally created by Facebook for describing data requirements on complex application data models. Visit <https://graphql.org> to learn more about GraphQL'.

r-genehummus 1.0.11
Propagated dependencies: r-stringr@1.6.0 r-rentrez@1.2.4 r-httr@1.4.7 r-dplyr@1.1.4 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/NCBI-Hackathons/GeneHummus
Licenses: Expat
Build system: r
Synopsis: Pipeline to Define Gene Families in Legumes and Beyond
Description:

This package provides a pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) <doi:10.1101/436659> for more information and examples.

r-glossa 1.2.4
Propagated dependencies: r-zip@2.3.3 r-waiter@0.2.5-1.927501b r-tidyterra@1.0.0 r-terra@1.8-86 r-svglite@2.2.2 r-sparkline@2.0 r-shinywidgets@0.9.0 r-shiny@1.11.1 r-sf@1.0-23 r-proc@1.19.0.1 r-mcp@0.3.4 r-markdown@2.0 r-leaflet@2.2.3 r-htmltools@0.5.8.1 r-ggplot2@4.0.1 r-geothinner@2.1.0 r-dt@0.34.0 r-dplyr@1.1.4 r-dbarts@0.9-32 r-bs4dash@2.3.5 r-blockcv@3.2-0 r-automap@1.1-20
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/iMARES-group/glossa
Licenses: GPL 3
Build system: r
Synopsis: User-Friendly 'shiny' App for Bayesian Species Distribution Models
Description:

This package provides a user-friendly shiny application for Bayesian machine learning analysis of marine species distributions. GLOSSA (Global Ocean Species Spatio-temporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales. Optionally, pseudo-absences can be generated within the environmental space using the external package flexsdm (not on CRAN), which can be downloaded from <https://github.com/sjevelazco/flexsdm>; this functionality is used conditionally when available and all core features work without it.

r-ggredist 0.0.4
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-palette@0.0.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/alarm-redist/ggredist
Licenses: Expat
Build system: r
Synopsis: Scales, Geometries, and Extensions of 'ggplot2' for Election Mapping
Description:

This package provides ggplot2 extensions for political map making. Implements new geometries for groups of simple feature geometries. Adds palettes and scales for red to blue color mapping and for discrete maps. Implements tools for easy label generation and placement, automatic map coloring, and themes.

r-gselection 0.1.0
Propagated dependencies: r-sam@1.1.3 r-penalized@0.9-53 r-gdata@3.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GSelection
Licenses: GPL 3
Build system: r
Synopsis: Genomic Selection
Description:

Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.

r-ggtrendline 1.0.3
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/PhDMeiwp/ggtrendline
Licenses: GPL 3
Build system: r
Synopsis: Add Trendline and Confidence Interval to 'ggplot'
Description:

Add trendline and confidence interval of linear or nonlinear regression model and show equation to ggplot as simple as possible. For a general overview of the methods used in this package, see Ritz and Streibig (2008) <doi:10.1007/978-0-387-09616-2> and Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.

r-gmpro 0.1.0
Propagated dependencies: r-transport@0.15-4 r-igraph@2.2.1 r-combinat@0.0-8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://arxiv.org/abs/2006.03284
Licenses: GPL 2
Build system: r
Synopsis: Graph Matching with Degree Profiles
Description:

This package provides functions for graph matching via nodes degree profiles are provided in this package. The models we can handle include Erdos-Renyi random graphs and stochastic block models(SBM). More details are in the reference paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <arXiv:2006.03284>.

r-ggfoundry 0.3.1
Propagated dependencies: r-rlang@1.1.6 r-lifecycle@1.0.4 r-grimport2@0.3-3 r-ggplot2@4.0.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/cgoo4/ggfoundry
Licenses: Expat
Build system: r
Synopsis: Shape Foundry & Geom for 'ggplot2'
Description:

This package provides a ggplot2 extension that supports arbitrary hand-crafted colourable & fillable shapes. New shapes may be feature requested via a Github issue.

r-gaselect 1.0.25
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://github.com/dakep/gaselect
Licenses: GPL 2+
Build system: r
Synopsis: Genetic Algorithm (GA) for Variable Selection from High-Dimensional Data
Description:

This package provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.

r-gisintegration 1.0
Propagated dependencies: r-tm@0.7-16 r-syn@0.1.0 r-stringr@1.6.0 r-shapefiles@0.7.2 r-sf@1.0-23 r-recordlinkage@0.4-12.6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GISINTEGRATION
Licenses: GPL 3
Build system: r
Synopsis: GIS Integration
Description:

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package GISINTEGRATION offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

r-gsmx 1.3
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=GSMX
Licenses: GPL 2+
Build system: r
Synopsis: Multivariate Genomic Selection
Description:

Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.

r-ggvolcano 0.1.4
Propagated dependencies: r-shiny@1.11.1 r-golem@0.5.1 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-config@0.3.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=ggvolcano
Licenses: Expat
Build system: r
Synopsis: Publication-Ready Volcano Plots
Description:

This package provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

r-gofigr 1.1.3
Propagated dependencies: r-shinyjs@2.1.0 r-shiny@1.11.1 r-scriptname@1.0.1 r-rsvg@2.7.0 r-rstudioapi@0.17.1 r-readr@2.1.6 r-qrcode@0.3.0 r-magick@2.9.0 r-knitr@1.50 r-jsonlite@2.0.0 r-httr@1.4.7 r-ggplotify@0.1.3 r-getpass@0.2-4 r-digest@0.6.39 r-cowplot@1.2.0 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/GoFigr/gofigR
Licenses: Expat
Build system: r
Synopsis: Client for 'GoFigr.io'
Description:

Integrates with your RMarkdown documents to automatically publish figures to the <https://GoFigr.io> service. Supports both knitr and interactive execution within RStudio'.

r-gamselbayes 2.0-3
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=gamselBayes
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Generalized Additive Model Selection
Description:

Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) <doi:10.1007/s10182-023-00490-y>.

r-glrth 0.2.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gLRTH
Licenses: GPL 3
Build system: r
Synopsis: Genome-Wide Association and Linkage Analysis under Heterogeneity
Description:

Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.

r-ggmridge 1.5
Propagated dependencies: 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=GGMridge
Licenses: GPL 2
Build system: r
Synopsis: Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation
Description:

Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).

r-groqr 0.0.3
Propagated dependencies: r-shinywidgets@0.9.0 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-miniui@0.1.2 r-jsonlite@2.0.0 r-httr@1.4.7 r-clipr@0.8.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/GabrielKaiserQFin/groqR
Licenses: GPL 3+
Build system: r
Synopsis: Coding Assistant using the Fast AI Inference 'Groq'
Description:

This package provides a comprehensive suite of functions and RStudio Add-ins leveraging the capabilities of open-source Large Language Models (LLMs) to support R developers. These functions offer a range of utilities, including text rewriting, translation, and general query capabilities. Additionally, the programming-focused functions provide assistance with debugging, translating, commenting, documenting, and unit testing code, as well as suggesting variable and function names, thereby streamlining the development process.

r-gformulaice 1.1.1
Propagated dependencies: r-stringr@1.6.0 r-speedglm@0.3-5 r-rlang@1.1.6 r-reshape2@1.4.5 r-nnet@7.3-20 r-magrittr@2.0.4 r-hmisc@5.2-4 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 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=gfoRmulaICE
Licenses: Expat
Build system: r
Synopsis: Parametric Iterative Conditional Expectation G-Formula
Description:

This package implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (Wen, Young, Robins, and Hernán (2020) <doi: 10.1111/biom.13321>). Both singly robust and doubly robust ICE estimators based on parametric models are available. The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring, and competing events. The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.

r-glmtrans 2.1.0
Propagated dependencies: r-glmnet@4.1-10 r-ggplot2@4.0.1 r-formatr@1.14 r-foreach@1.5.2 r-doparallel@1.0.17 r-caret@7.0-1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmtrans
Licenses: GPL 2
Build system: r
Synopsis: Transfer Learning under Regularized Generalized Linear Models
Description:

We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.".

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