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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-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-gwlasso 1.0.2
Propagated dependencies: r-tidyr@1.3.1 r-sf@1.0-23 r-rlang@1.1.6 r-progress@1.2.3 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-gwmodel@2.4-1 r-glmnet@4.1-10 r-ggside@0.4.1 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/nibortolum/GWlasso
Licenses: Expat
Build system: r
Synopsis: Geographically Weighted Lasso
Description:

This package performs geographically weighted Lasso regressions. Find optimal bandwidth, fit a geographically weighted lasso or ridge regression, and make predictions. These methods are specially well suited for ecological inferences. Bandwidth selection algorithm is from A. Comber and P. Harris (2018) <doi:10.1007/s10109-018-0280-7>.

r-gese 2.0.1
Propagated dependencies: r-kinship2@1.9.6.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GESE
Licenses: GPL 2
Build system: r
Synopsis: Gene-Based Segregation Test
Description:

This package implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.

r-gtests 0.2
Propagated dependencies: r-ade4@1.7-23
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gTests
Licenses: GPL 2+
Build system: r
Synopsis: Graph-Based Two-Sample Tests
Description:

Four graph-based tests are provided for testing whether two samples are from the same distribution. It works for both continuous data and discrete data.

r-glme 0.1.0
Propagated dependencies: r-reshape@0.8.10 r-nlme@3.1-168 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glme
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Linear Mixed Effects Models
Description:

This package provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference.

r-genbarcode 1.2.8
Propagated dependencies: r-visnetwork@2.1.4 r-venndiagram@1.7.3 r-stringdist@0.9.15 r-shortread@1.68.0 r-shiny@1.11.1 r-s4vectors@0.48.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-phangorn@2.12.1 r-network@1.19.0 r-igraph@2.2.1 r-ggtree@4.0.1 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-ggnetwork@0.5.14 r-future-apply@1.20.0 r-future@1.68.0 r-futile-logger@1.4.3 r-dplyr@1.1.4 r-biostrings@2.78.0 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=genBaRcode
Licenses: LGPL 2.0+
Build system: r
Synopsis: Analysis and Visualization Tools for Genetic Barcode Data
Description:

This package provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).

r-gglasso 1.6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/archer-yang-lab/gglasso
Licenses: GPL 2
Build system: r
Synopsis: Group Lasso Penalized Learning Using a Unified BMD Algorithm
Description:

This package provides a unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) <doi:10.1007/s11222-014-9498-5>.

r-gradient 1.0.1
Propagated dependencies: r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/bmgaldo/graDiEnt
Licenses: Expat
Build system: r
Synopsis: Stochastic Quasi-Gradient Differential Evolution Optimization
Description:

An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for difficult optimization problems under a tight function evaluation budget. This package can run SQG-DE in parallel and sequentially.

r-grape 0.1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GRAPE
Licenses: GPL 2
Build system: r
Synopsis: Gene-Ranking Analysis of Pathway Expression
Description:

Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing the consensus behavior of biological pathways in the form of a template, and for quantifying the extent to which individual samples deviate from the template. GRAPE templates are based only on the relative rankings of the genes within the pathway and can be used for classification of tissue types or disease subtypes. GRAPE can be used to represent gene-expression samples as vectors of pathway scores, where each pathway score indicates the departure from a given collection of reference samples. The resulting pathway- space representation can be used as the feature set for various applications, including survival analysis and drug-response prediction. Users of GRAPE should use the following citation: Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics, 18:317 (June 2017).

r-gvcr 0.4.0
Propagated dependencies: r-tibble@3.3.0 r-supernova@3.0.1 r-rlang@1.1.6 r-r6@2.6.1 r-magrittr@2.0.4 r-lme4@1.1-37 r-eda4treer@1.1.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gvcR
Licenses: GPL 3
Build system: r
Synopsis: Genotypic Variance Components
Description:

Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).

r-ggincerta 0.1.0
Propagated dependencies: r-withr@3.0.2 r-sf@1.0-23 r-scales@1.4.0 r-rlang@1.1.6 r-gtable@0.3.6 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/maggiexma/ggincerta
Licenses: Expat
Build system: r
Synopsis: Extend 'ggplot2' with Layers and Scales for Spatial Uncertainty Visualization
Description:

Provide specialized ggplot2 layers and scales for spatial uncertainty visualization, including bivariate choropleth maps, pixel maps, glyph maps, and exceedance probability maps.

r-ggplotassist 0.1.3
Propagated dependencies: r-tidyverse@2.0.0 r-tibble@3.3.0 r-stringr@1.6.0 r-shinywidgets@0.9.0 r-shinyace@0.4.4 r-shiny@1.11.1 r-scales@1.4.0 r-rstudioapi@0.17.1 r-moonbook@0.3.1 r-miniui@0.1.2 r-magrittr@2.0.4 r-ggthemes@5.1.0 r-ggplot2@4.0.1 r-gcookbook@2.0.1 r-editdata@0.1.8 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/cardiomoon/ggplotAssist
Licenses: GPL 3
Build system: r
Synopsis: 'RStudio' Addin for Teaching and Learning 'ggplot2'
Description:

An RStudio addin for teaching and learning making plot using the ggplot2 package. You can learn each steps of making plot by clicking your mouse without coding. You can get resultant code for the plot.

r-genomic-autocorr 1.0-1
Propagated dependencies: r-reshape@0.8.10 r-magrittr@2.0.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/chr1swallace/genomic.autocorr
Licenses: GPL 2
Build system: r
Synopsis: Models Dealing with Spatial Dependency in Genomic Data
Description:

Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.

r-ggmr 0.1.1
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=ggmr
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Gauss Markov Regression
Description:

This package implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.

r-ggplotgui 1.0.0
Propagated dependencies: r-stringr@1.6.0 r-shiny@1.11.1 r-readxl@1.4.5 r-readr@2.1.6 r-rcolorbrewer@1.1-3 r-plotly@4.11.0 r-haven@2.5.5 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/gertstulp/ggplotgui/
Licenses: GPL 3
Build system: r
Synopsis: Create Ggplots via a Graphical User Interface
Description:

Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.

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-gcite 0.11.0
Propagated dependencies: r-xml2@1.5.0 r-wordcloud@2.6 r-tm@0.7-16 r-rvest@1.0.5 r-pbapply@1.7-4 r-httr@1.4.7 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=gcite
Licenses: GPL 3
Build system: r
Synopsis: Google Citation Parser
Description:

Scrapes Google Citation pages and creates data frames of citations over time.

r-ghapps 1.1.1
Propagated dependencies: r-openssl@2.3.4 r-jose@1.2.1 r-gh@1.5.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://r-lib.r-universe.dev/ghapps
Licenses: Expat
Build system: r
Synopsis: Authenticate as a 'GitHub' App
Description:

GitHub apps provide a powerful way to manage fine grained programmatic access to specific git repositories, without having to create dummy users, and which are safer than a personal access token for automated tasks. This package extends the gh package to let you authenticate and interact with GitHub <https://docs.github.com/en/rest/overview> in R as an app.

r-hstats 1.2.2
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://github.com/ModelOriented/hstats/
Licenses: GPL 2+
Build system: r
Synopsis: Interaction Statistics
Description:

Fast, model-agnostic implementation of different H-statistics introduced by Jerome H. Friedman and Bogdan E. Popescu (2008) <doi:10.1214/07-AOAS148>. These statistics quantify interaction strength per feature, feature pair, and feature triple. The package supports multi-output predictions and can account for case weights. In addition, several variants of the original statistics are provided. The shape of the interactions can be explored through partial dependence plots or individual conditional expectation plots. DALEX explainers, meta learners ('mlr3', tidymodels', caret') and most other models work out-of-the-box.

r-hdar 1.0.7
Propagated dependencies: r-stringr@1.6.0 r-scales@1.4.0 r-r6@2.6.1 r-progress@1.2.3 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-httr2@1.2.1 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://www.wekeo.eu/
Licenses: GPL 3
Build system: r
Synopsis: 'REST' API Client for Accessing Data on 'WEkEO HDA V2'
Description:

This package provides seamless access to the WEkEO Harmonised Data Access (HDA) API, enabling users to query, download, and process data efficiently from the HDA platform. With hdar', researchers and data scientists can integrate the extensive HDA datasets into their R workflows, enhancing their data analysis capabilities. Comprehensive information on the API functionality and usage is available at <https://gateway.prod.wekeo2.eu/hda-broker/docs>.

r-hydra 0.1.0
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hydra
Licenses: GPL 2
Build system: r
Synopsis: Hyperbolic Embedding
Description:

Calculate an optimal embedding of a set of data points into low-dimensional hyperbolic space. This uses the strain-minimizing hyperbolic embedding of Keller-Ressel and Nargang (2019), see <arXiv:1903.08977>.

r-hicociety 0.1.38
Propagated dependencies: r-txdb-mmusculus-ucsc-mm10-knowngene@3.10.0 r-txdb-hsapiens-ucsc-hg38-knowngene@3.22.0 r-strawr@0.0.92 r-signal@1.8-1 r-shape@1.4.6.1 r-s4vectors@0.48.0 r-rcpp@1.1.0 r-pracma@2.4.6 r-org-mm-eg-db@3.22.0 r-org-hs-eg-db@3.22.0 r-iranges@2.44.0 r-igraph@2.2.1 r-hicocietyexample@1.0.0 r-ggraph@2.2.2 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-foreach@1.5.2 r-fitdistrplus@1.2-4 r-doparallel@1.0.17 r-biomart@2.66.0 r-biocmanager@1.30.27 r-biocgenerics@0.56.0 r-annotationdbi@1.72.0
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=HiCociety
Licenses: GPL 3
Build system: r
Synopsis: Inferring Chromatin Interaction Modules from 3C-Based Data
Description:

Identifies chromatin interaction modules by constructing a Hi-C contact network based on statistically significant interactions, followed by network clustering. The method enables comparison of module connectivity across two Hi-C datasets and is capable of detecting cell-type-specific regulatory modules. By integrating network analysis with chromatin conformation data, this approach provides insights into the spatial organization of the genome and its functional implications in gene regulation. Author: Sora Yoon (2025) <https://github.com/ysora/HiCociety>.

r-hnmf 1.0
Propagated dependencies: r-rasterimage@0.4.0 r-oro-nifti@0.11.4 r-nnls@1.6 r-nmf@0.28 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hNMF
Licenses: GPL 3
Build system: r
Synopsis: Hierarchical Non-Negative Matrix Factorization
Description:

Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.

r-hatchr 1.0.1
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-ggtext@0.1.2 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://bmait101.github.io/hatchR/
Licenses: GPL 3
Build system: r
Synopsis: Predict Fish Hatch and Emergence Timing
Description:

Predict hatch and emergence timing for a wide range of wild fishes using the effective value framework (Sparks et al., (2019) <DOI:10.1139/cjfas-2017-0468>). hatchR offers users access to established phenological models and the flexibility to incorporate custom parameterizations using external datasets.

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