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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-gmac 3.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GMAC
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Genomic Mediation Analysis with Adaptive Confounding Adjustment
Description:

This package performs genomic mediation analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/gr.216754.116>. It implements large scale mediation analysis and adaptively selects potential confounding variables to adjust for each mediation test from a pool of candidate confounders. The package is tailored for but not limited to genomic mediation analysis (e.g., cis-gene mediating trans-gene regulation pattern where an eQTL, its cis-linking gene transcript, and its trans-gene transcript play the roles as treatment, mediator and the outcome, respectively), restricting to scenarios with the presence of cis-association (i.e., treatment-mediator association) and random eQTL (i.e., treatment).

r-garray 1.1.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=garray
Licenses: GPL 3
Build system: r
Synopsis: Generalized Array Arithmetic for Ragged Arrays with Named Margins
Description:

Organize a so-called ragged array as generalized arrays, which is simply an array with sub-dimensions denoting the subdivision of dimensions (grouping of members within dimensions). By the margins (names of dimensions and sub-dimensions) in generalized arrays, operators and utility functions provided in this package automatically match the margins, doing map-reduce style parallel computation along margins. Generalized arrays are also cooperative to R's native functions that work on simple arrays.

r-gcestim 0.1.0
Propagated dependencies: r-zoo@1.8-14 r-viridis@0.6.5 r-simstudy@0.9.2 r-shinywidgets@0.9.0 r-shinydashboardplus@2.0.6 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-rsolnp@2.0.1 r-rlang@1.1.6 r-readxl@1.4.5 r-pracma@2.4.6 r-plotly@4.11.0 r-pathviewr@1.1.8 r-optimx@2025-4.9 r-optimparallel@1.0-2 r-miniui@0.1.2 r-meboot@1.5 r-magrittr@2.0.4 r-lbfgsb3c@2024-3.5 r-lbfgs@1.2.1.2 r-latex2exp@0.9.6 r-hdrcde@3.4 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ggdist@3.3.3 r-dt@0.34.0 r-downlit@0.4.5 r-data-table@1.17.8 r-clustergeneration@1.3.8 r-bayestestr@0.17.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/jorgevazcabral/GCEstim
Licenses: GPL 3
Build system: r
Synopsis: Regression Coefficients Estimation Using the Generalized Cross Entropy
Description:

Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) <doi:10.1007/978-3-031-97589-9_21>). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) <doi:10.1007/978-3-030-26036-1_2> "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) <doi:10.1080/03610918.2022.2057540>) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

r-g-ridge 1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=g.ridge
Licenses: GPL 2
Build system: r
Synopsis: Generalized Ridge Regression for Linear Models
Description:

Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.

r-gpvam 3.2-0
Propagated dependencies: r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-patchwork@1.3.2 r-numderiv@2016.8-1.1 r-matrix@1.7-4 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=GPvam
Licenses: GPL 2
Build system: r
Synopsis: Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling
Description:

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

r-grace 0.5.3
Propagated dependencies: r-scalreg@1.0.1 r-mass@7.3-65 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: http://onlinelibrary.wiley.com/doi/10.1111/biom.12418/abstract
Licenses: GPL 3
Build system: r
Synopsis: Graph-Constrained Estimation and Hypothesis Tests
Description:

Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.

r-getspres 0.2.0
Propagated dependencies: r-rcolorbrewer@1.1-3 r-plotrix@3.8-13 r-metafor@4.8-0 r-dplyr@1.1.4 r-colorspace@2.1-2 r-colorramps@2.3.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://magosil86.github.io/getspres/
Licenses: Expat
Build system: r
Synopsis: SPRE Statistics for Exploring Heterogeneity in Meta-Analysis
Description:

An implementation of SPRE (standardised predicted random-effects) statistics in R to explore heterogeneity in genetic association meta- analyses, as described by Magosi et al. (2019) <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision weighted residuals that indicate the direction and extent with which individual study-effects in a meta-analysis deviate from the average genetic effect. Overly influential positive outliers have the potential to inflate average genetic effects in a meta-analysis whilst negative outliers might lower or change the direction of effect. See the getspres website for documentation and examples <https://magosil86.github.io/getspres/>.

r-glmmrbase 1.2.1
Propagated dependencies: r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-r6@2.6.1 r-matrix@1.7-4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/samuel-watson/glmmrBase
Licenses: GPL 2+
Build system: r
Synopsis: Generalised Linear Mixed Models in R
Description:

Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more.

r-gfboost 0.1.1
Propagated dependencies: r-pcapp@2.0-5 r-mvtnorm@1.3-3 r-mboost@2.9-11
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gfboost
Licenses: GPL 2+
Build system: r
Synopsis: Gradient-Free Gradient Boosting
Description:

Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL <https://oops.uni-oldenburg.de/id/eprint/4290>').

r-gckrig 1.1.8
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=gcKrig
Licenses: GPL 2+
Build system: r
Synopsis: Analysis of Geostatistical Count Data using Gaussian Copulas
Description:

This package provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.

r-gscounts 0.1-4
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/tobiasmuetze/gscounts
Licenses: GPL 2+
Build system: r
Synopsis: Group Sequential Designs with Negative Binomial Outcomes
Description:

Design and analysis of group sequential designs for negative binomial outcomes, as described by T Mütze, E Glimm, H Schmidli, T Friede (2018) <doi:10.1177/0962280218773115>.

r-genekitr 1.2.8
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-stringi@1.8.7 r-rlang@1.1.6 r-openxlsx@4.2.8.1 r-magrittr@2.0.4 r-igraph@2.2.1 r-ggvenn@0.1.19 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-geneset@0.2.7 r-fst@0.9.8 r-europepmc@0.4.3 r-dplyr@1.1.4 r-clusterprofiler@4.18.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://www.genekitr.fun/
Licenses: GPL 3
Build system: r
Synopsis: Gene Analysis Toolkit
Description:

This package provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.

r-gamlr 1.13-8
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/TaddyLab/gamlr
Licenses: GPL 3
Build system: r
Synopsis: Gamma Lasso Regression
Description:

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization', <arXiv:1308.5623>.

r-gridonclusters 0.3.2
Propagated dependencies: r-rdpack@2.6.4 r-rcpp@1.1.0 r-plotrix@3.8-13 r-mclust@6.1.2 r-fossil@0.4.0 r-dqrng@0.4.1 r-cluster@2.1.8.1 r-ckmeans-1d-dp@4.3.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GridOnClusters
Licenses: LGPL 3+
Build system: r
Synopsis: Multivariate Joint Grid Discretization
Description:

Discretize multivariate continuous data using a grid to capture the joint distribution that preserves clusters in original data. It can handle both labeled or unlabeled data. Both published methods (Wang et al 2020) <doi:10.1145/3388440.3412415> and new methods are included. Joint grid discretization can prepare data for model-free inference of association, function, or causality.

r-gpl2025 1.0.1
Propagated dependencies: 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=GPL2025
Licenses: Artistic License 2.0
Build system: r
Synopsis: Convert Chip ID of the GPL2015 into GeneBank Accession and ENTREZID
Description:

Convert the chip ID of GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> to GeneBank Accession and ENTREZID <http://www.ncbi.nlm.nih.gov/gene>.

r-gofreg 1.0.0
Propagated dependencies: r-survival@3.8-3 r-r6@2.6.1 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/gkremling/gofreg
Licenses: Expat
Build system: r
Synopsis: Bootstrap-Based Goodness-of-Fit Tests for Parametric Regression
Description:

This package provides statistical methods to check if a parametric family of conditional density functions fits to some given dataset of covariates and response variables. Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>, Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method.

r-gammslice 2.0-2
Propagated dependencies: r-mgcv@1.9-4 r-lattice@0.22-7 r-kernsmooth@2.23-26
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gammSlice
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Additive Mixed Model Analysis via Slice Sampling
Description:

Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.

r-gcplyr 1.12.0
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://mikeblazanin.github.io/gcplyr/
Licenses: Expat
Build system: r
Synopsis: Wrangle and Analyze Growth Curve Data
Description:

Easy wrangling and model-free analysis of microbial growth curve data, as commonly output by plate readers. Tools for reshaping common plate reader outputs into tidy formats and merging them with design information, making data easy to work with using gcplyr and other packages. Also streamlines common growth curve processing steps, like smoothing and calculating derivatives, and facilitates model-free characterization and analysis of growth data. See methods at <https://mikeblazanin.github.io/gcplyr/>.

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-geodadata 0.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/spatialanalysis/geodaData
Licenses: CC0
Build system: r
Synopsis: Spatial Analysis Datasets for Teaching
Description:

Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the GeoDa software workbook and data site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin and team at the University of Chicago. Datasets are stored as sf objects.

r-graphicalevidence 1.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 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://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-ggspark 0.0.2
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/marcboschmatas/ggspark
Licenses: GPL 2+
Build system: r
Synopsis: 'ggplot2' Functions to Create Tufte Style Sparklines
Description:

This package provides functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.

r-gk2011 0.1.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/leeper/GK2011
Licenses: GPL 2+
Build system: r
Synopsis: Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
Description:

Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.

r-glmmpen 1.5.4.8
Propagated dependencies: r-survival@3.8-3 r-stringr@1.6.0 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-reshape2@1.4.5 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ncvreg@3.16.0 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-mass@7.3-65 r-lme4@1.1-37 r-ggplot2@4.0.1 r-bigmemory@4.6.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmmPen
Licenses: GPL 2+
Build system: r
Synopsis: High Dimensional Penalized Generalized Linear Mixed Models (pGLMM)
Description:

Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>. The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the ncvreg package (Breheny and Huang (2011) <doi:10.1214/10-AOAS388>) and grpreg package (Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>), with appropriate modifications to account for the estimation and penalization of the random effects. The ncvreg and grpreg packages also describe the MCP, SCAD, and LASSO penalties.

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