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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-glcmtextures 0.6.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://ailich.github.io/GLCMTextures/
Licenses: GPL 3+
Build system: r
Synopsis: GLCM Textures of Raster Layers
Description:

Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.

r-glogis 1.0-2
Propagated dependencies: r-zoo@1.8-14 r-sandwich@3.1-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glogis
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Fitting and Testing Generalized Logistic Distributions
Description:

This package provides tools for the generalized logistic distribution (Type I, also known as skew-logistic distribution), encompassing basic distribution functions (p, q, d, r, score), maximum likelihood estimation, and structural change methods.

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-geno2proteo 0.0.6
Dependencies: perl@5.36.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=geno2proteo
Licenses: Artistic License 2.0
Build system: r
Synopsis: Finding the DNA and Protein Sequences of Any Genomic or Proteomic Loci
Description:

Using the DNA sequence and gene annotation files provided in ENSEMBL <https://www.ensembl.org/index.html>, the functions implemented in the package try to find the DNA sequences and protein sequences of any given genomic loci, and to find the genomic coordinates and protein sequences of any given protein locations, which are the frequent tasks in the analysis of genomic and proteomic data.

r-genmarkov 0.2.1
Propagated dependencies: r-nnet@7.3-20 r-maxlik@1.5-2.1 r-matrixcalc@1.0-6 r-hmisc@5.2-4 r-fastdummies@1.7.5 r-alabama@2023.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GenMarkov
Licenses: GPL 2+
Build system: r
Synopsis: Multivariate Markov Chains
Description:

This package provides routines to estimate the Mixture Transition Distribution Model based on Raftery (1985) <http://www.jstor.org/stable/2345788> and Nicolau (2014) <doi:10.1111/sjos.12087> specifications, for multivariate data. Additionally, provides a function for the estimation of a new model for multivariate non-homogeneous Markov chains. This new specification, Generalized Multivariate Markov Chains (GMMC) was proposed by Carolina Vasconcelos and Bruno Damasio and considers (continuous or discrete) covariates exogenous to the Markov chain.

r-geocomplexity 0.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://ausgis.github.io/geocomplexity/
Licenses: GPL 3
Build system: r
Synopsis: Mitigating Spatial Bias Through Geographical Complexity
Description:

The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.

r-glca 1.4.2
Propagated dependencies: r-rcpp@1.1.0 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://kim0sun.github.io/glca/
Licenses: GPL 3
Build system: r
Synopsis: An R Package for Multiple-Group Latent Class Analysis
Description:

Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).

r-glmm-hp 1.0-0
Propagated dependencies: r-vegan@2.7-2 r-mumin@1.48.11 r-lme4@1.1-37 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/laijiangshan/glmm.hp
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models
Description:

Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.

r-go2bigq 2.0.1
Propagated dependencies: r-rmpfr@1.1-2 r-gmp@0.7-5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=go2bigq
Licenses: LGPL 3
Build system: r
Synopsis: Convert Large Numbers to Bigq Format
Description:

This function converts mfpr, numeric, or character strings representing numbers to bigq format without loss of precision.

r-ggclassification 0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GGClassification
Licenses: GPL 2+
Build system: r
Synopsis: Gabriel Graph Based Large-Margin Classifiers
Description:

This package contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.

r-glioblastomaehrsdata 1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/Samumar4321/glioblastomaEHRsData
Licenses: GPL 3
Build system: r
Synopsis: Descriptive Analysis on Three Glioblastoma EHRs Datasets
Description:

This package provides functions to load and analyze three open Electronic Health Records (EHRs) datasets of patients diagnosed with glioblastoma, previously released under the Creative Common Attribution 4.0 International (CC BY 4.0) license. Users can generate basic descriptive statistics, frequency tables and save descriptive summary tables, as well as create and export univariate or bivariate plots. The package is designed to work with the included datasets and to facilitate quick exploratory data analysis and reporting. More information about these three datasets of EHRs of patients with glioblastoma can be found in this article: Gabriel Cerono, Ombretta Melaiu, and Davide Chicco, Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March 2024). <doi:10.1007/s41666-023-00138-1>.

r-ggtikz 0.1.5
Propagated dependencies: r-tikzdevice@0.12.6 r-stringr@1.6.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/osthomas/ggtikz
Licenses: Expat
Build system: r
Synopsis: Post-Process 'ggplot2' Plots with 'TikZ' Code Using Plot Coordinates
Description:

Annotation of ggplot2 plots with arbitrary TikZ code, using absolute data or relative plot coordinates.

r-geomaroc 0.1.1
Propagated dependencies: r-sf@1.0-23 r-jsonlite@2.0.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/AmineAndam04/R-geomaroc
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Easily Visualize Geographic Data of Morocco
Description:

This package provides tools to easily visualize geographic data of Morocco. This package interacts with data available through the geomarocdata package, which is available in a drat repository. The size of the geomarocdata package is approximately 12 MB.

r-genpwr 1.0.4
Propagated dependencies: r-nleqslv@3.3.5 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=genpwr
Licenses: GPL 3
Build system: r
Synopsis: Power Calculations Under Genetic Model Misspecification
Description:

Power and sample size calculations for genetic association studies allowing for misspecification of the model of genetic susceptibility. "Hum Hered. 2019;84(6):256-271.<doi:10.1159/000508558>. Epub 2020 Jul 28." Power and/or sample size can be calculated for logistic (case/control study design) and linear (continuous phenotype) regression models, using additive, dominant, recessive or degree of freedom coding of the genetic covariate while assuming a true dominant, recessive or additive genetic effect. In addition, power and sample size calculations can be performed for gene by environment interactions. These methods are extensions of Gauderman (2002) <doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973> and are described in: Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification. American Society of Human Genetics. October 2018, San Diego.

r-glmcat 1.0.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/ylleonv/GLMcat
Licenses: GPL 3
Build system: r
Synopsis: Generalized Linear Models for Categorical Responses
Description:

In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix. The package accompanies the paper "GLMcat: An R Package for Generalized Linear Models for Categorical Responses" in the Journal of Statistical Software, Volume 114, Issue 9 (see <doi:10.18637/jss.v114.i09>).

r-ginici 0.1.3
Propagated dependencies: r-ggrepel@0.9.6 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-desctools@0.99.60
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/novidu/giniCI
Licenses: GPL 3+
Build system: r
Synopsis: Gini-Based Composite Indicators
Description:

An implementation of Gini-based weighting approaches in constructing composite indicators, providing functionalities for normalization, aggregation, and ranking comparison.

r-gramevol 2.1-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/fnoorian/gramEvol/
Licenses: GPL 2+
Build system: r
Synopsis: Grammatical Evolution for R
Description:

This package provides a native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.

r-geodregr 0.2.0
Propagated dependencies: r-zipfr@0.6-70 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/hayoungshin1/GeodRegr
Licenses: GPL 3
Build system: r
Synopsis: Geodesic Regression
Description:

This package provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y>.

r-gscalca 0.0.5
Propagated dependencies: r-stringr@1.6.0 r-psych@2.5.6 r-progress@1.2.3 r-nnet@7.3-20 r-mass@7.3-65 r-gridextra@2.3 r-ggplot2@4.0.1 r-foreach@1.5.2 r-fclust@2.1.3 r-fastdummies@1.7.5 r-dosnow@1.0.20 r-devtools@2.4.6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/hee6904/gscaLCA
Licenses: GPL 3
Build system: r
Synopsis: Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression
Description:

Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>. It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.

r-ggvolc 0.1.0
Propagated dependencies: r-gridextra@2.3 r-ggtext@0.1.2 r-ggrepel@0.9.6 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://cran.r-project.org/package=ggvolc
Licenses: Expat
Build system: r
Synopsis: Create Volcano Plots for Differential Gene Expression Data
Description:

This package provides functionality to create customizable volcano plots for visualizing differential gene expression analysis results. The package offers options to highlight genes of interest, adjust significance thresholds, customize colors, and add informative labels. Designed specifically for RNA-seq data analysis workflows.

r-ggresidpanel 0.3.0
Propagated dependencies: r-stringr@1.6.0 r-qqplotr@0.0.7 r-plotly@4.11.0 r-mass@7.3-65 r-ggplot2@4.0.1 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://goodekat.github.io/ggResidpanel/
Licenses: Expat
Build system: r
Synopsis: Panels and Interactive Versions of Diagnostic Plots using 'ggplot2'
Description:

An R package for creating panels of diagnostic plots for residuals from a model using ggplot2 and plotly to analyze residuals and model assumptions from a variety of viewpoints. It also allows for the creation of interactive diagnostic plots.

r-gwpcor 0.1.8
Dependencies: proj@9.3.1 geos@3.12.1 gdal@3.8.2
Propagated dependencies: r-sp@2.2-0 r-sf@1.0-23 r-pracma@2.4.6 r-geodist@0.1.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/gwpcor/GWpcor
Licenses: GPL 3
Build system: r
Synopsis: Geographically Weighted Partial Correlation Coefficient
Description:

This package implements a geographically weighted partial correlation which is an extension from gwss() function in the GWmodel package (Percival and Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).

r-gear 0.3.4
Propagated dependencies: r-rcpp@1.1.0 r-optimx@2025-4.9 r-autoimage@2.2.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gear
Licenses: GPL 2+
Build system: r
Synopsis: Geostatistical Analysis in R
Description:

This package implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).

r-geocmeans 0.3.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/JeremyGelb/geocmeans
Licenses: GPL 2
Build system: r
Synopsis: Implementing Methods for Spatial Fuzzy Unsupervised Classification
Description:

This package provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

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