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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-ggordiplots 0.4.3
Propagated dependencies: r-vegan@2.7-2 r-glue@1.8.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/jfq3/ggordiplots
Licenses: GPL 2+
Build system: r
Synopsis: Make 'ggplot2' Versions of Vegan's Ordiplots
Description:

The vegan package includes several functions for adding features to ordination plots: ordiarrows(), ordiellipse(), ordihull(), ordispider() and ordisurf(). This package adds these same features to ordination plots made with ggplot2'. In addition, gg_ordibubble() sizes points relative to the value of an environmental variable.

r-greencrab-toolkit 0.2
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-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=greencrab.toolkit
Licenses: GPL 3
Build system: r
Synopsis: Run 'Stan' Models to Interpret Green Crab Monitoring Assessments
Description:

These Bayesian models written in the Stan probabilistic language can be used to interpret green crab trapping and environmental DNA monitoring data, either independently or jointly. Detailed model information is found in Keller (2022) <doi:10.1002/eap.2561>.

r-gvarx 1.4
Propagated dependencies: r-xts@0.14.1 r-vars@1.6-1 r-urca@1.3-4 r-tsdyn@11.0.5.2 r-strucchange@1.5-4 r-sandwich@3.1-1 r-lubridate@1.9.4 r-lmtest@0.9-40
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GVARX
Licenses: GPL 2+
Build system: r
Synopsis: Perform Global Vector Autoregression Estimation and Inference
Description:

Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.

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-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-gginnards 0.2.0-2
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://docs.r4photobiology.info/gginnards/
Licenses: GPL 2+
Build system: r
Synopsis: Explore the Innards of 'ggplot2' Objects
Description:

Extensions to ggplot2 providing low-level debug tools: statistics and geometries echoing their data argument. Layer manipulation: deletion, insertion, extraction and reordering of layers. Deletion of unused variables from the data object embedded in "ggplot" objects.

r-gsdesign2 1.1.7
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-survival@3.8-3 r-rcpp@1.1.0 r-r2rtf@1.3.0 r-npsurvss@1.1.0 r-mvtnorm@1.3-3 r-gt@1.3.0 r-gsdesign@3.8.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://merck.github.io/gsDesign2/
Licenses: GPL 3
Build system: r
Synopsis: Group Sequential Design with Non-Constant Effect
Description:

The goal of gsDesign2 is to enable fixed or group sequential design under non-proportional hazards. To enable highly flexible enrollment, time-to-event and time-to-dropout assumptions, gsDesign2 offers piecewise constant enrollment, failure rates, and dropout rates for a stratified population. This package includes three methods for designs: average hazard ratio, weighted logrank tests in Yung and Liu (2019) <doi:10.1111/biom.13196>, and MaxCombo tests. Substantial flexibility on top of what is in the gsDesign package is intended for selecting boundaries.

r-gaipe 1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://www.r-project.org
Licenses: GPL 2+
Build system: r
Synopsis: Graphical Extension with Accuracy in Parameter Estimation (GAIPE)
Description:

This package implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA.

r-gumboot 1.0.2
Propagated dependencies: r-stringr@1.6.0 r-reshape2@1.4.5 r-ncdf4@1.24 r-lubridate@1.9.4 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=gumboot
Licenses: GPL 3
Build system: r
Synopsis: Bootstrap Analyses of Sampling Uncertainty in Goodness-of-Fit Statistics
Description:

Uses jackknife and bootstrap methods to quantify the sampling uncertainty in goodness-of-fit statistics. Full details are in Clark et al. (2021), "The abuse of popular performance metrics in hydrologic modeling", Water Resources Research, <doi:10.1029/2020WR029001>.

r-gstar 0.1.0
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-reshape2@1.4.5 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=gstar
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Generalized Space-Time Autoregressive Model
Description:

Multivariate time series analysis based on Generalized Space-Time Autoregressive Model by Ruchjana et al.(2012) <doi:10.1063/1.4724118>.

r-gibble 0.4.0
Propagated dependencies: r-tibble@3.3.0 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://github.com/mdsumner/gibble
Licenses: GPL 3
Build system: r
Synopsis: Geometry Decomposition
Description:

Build a map of path-based geometry, this is a simple description of the number of parts in an object and their basic structure. Translation and restructuring operations for planar shapes and other hierarchical types require a data model with a record of the underlying relationships between elements. The gibble() function creates a geometry map, a simple record of the underlying structure in path-based hierarchical types. There are methods for the planar shape types in the sf and sp packages and for types in the trip and silicate packages.

r-globpso 1.3.1
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=globpso
Licenses: GPL 3
Build system: r
Synopsis: Swarm Intelligence Optimization
Description:

This package provides a fast and flexible general-purpose implementation of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving global minimization problems is provided. It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) <doi:10.1109/MHS.1995.494215>, Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) <doi:10.1109/CEC.2004.1330875>, Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) <doi:10.1007/s11721-014-0095-1>, Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) <doi:10.1109/TCYB.2014.2322602> and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) <doi:10.1016/j.asoc.2024.111913>. For the DE algorithm, six types in Storn, R. & Price, K. (1997) <doi:10.1023/A:1008202821328> are included: DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and DE/rand_to-best/2.

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-gsda 1.0
Propagated dependencies: r-msigdbr@25.1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GSDA
Licenses: GPL 2+
Build system: r
Synopsis: Gene Set Distance Analysis (GSDA)
Description:

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

r-gps 1.2
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/ZheyuanLi/gps
Licenses: GPL 3
Build system: r
Synopsis: General P-Splines
Description:

General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) <arXiv:2201.06808>. Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) <doi:10.1214/ss/1038425655>. They are also related to the O-splines of O'Sullivan (1986) <doi:10.1214/ss/1177013525> via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) <doi:10.1007/s11222-022-10178-z>. It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework.

r-gephiforr 0.1.1
Propagated dependencies: r-rdpack@2.6.4 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GephiForR
Licenses: Expat
Build system: r
Synopsis: 'Gephi' Network Visualization
Description:

This package implements key features of Gephi for network visualization, including ForceAtlas2 (with LinLog mode), network scaling, and network rotations. It also includes easy network visualization tools such as edge and node color assignment for recreating Gephi'-style graphs in R. The package references layout algorithms developed by Jacomy, M., Venturini T., Heymann S., and Bastian M. (2014) <doi:10.1371/journal.pone.0098679> and Noack, A. (2009) <doi:10.48550/arXiv.0807.4052>.

r-ggbrace 0.1.2
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/NicolasH2/ggbrace
Licenses: Expat
Build system: r
Synopsis: Curly Braces for 'ggplot2'
Description:

This package provides curly braces and square brackets in ggplot2 plus matching text. stat_brace() plots braces/brackets to embrace data. stat_bracetext() plots corresponding text, fitting to the braces from stat_brace().

r-goxygen 1.4.5
Dependencies: pandoc@2.19.2 pandoc@2.19.2
Propagated dependencies: r-yaml@2.3.10 r-withr@3.0.2 r-stringi@1.8.7 r-pander@0.6.6 r-gms@0.31.2 r-citation@0.12.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/pik-piam/goxygen
Licenses: FreeBSD
Build system: r
Synopsis: In-Code Documentation for 'GAMS'
Description:

This package provides a collection of tools which extract a model documentation from GAMS code and comments. In order to use the package you need to install pandoc and pandoc-citeproc first (<https://pandoc.org/>).

r-gmtfd 0.1.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-mass@7.3-65 r-gfdmcv@0.1.0 r-foreach@1.5.2 r-fda@6.3.0 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=gmtFD
Licenses: LGPL 2.0 LGPL 3 GPL 2 GPL 3
Build system: r
Synopsis: General Multiple Tests for Univariate and Multivariate Functional Data
Description:

The multiple contrast tests for univariate were proposed by Munko, Ditzhaus, Pauly, Smaga, and Zhang (2023) <doi:10.48550/arXiv.2306.15259>. Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/arXiv.2406.01242>. These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.

r-gcalignr 1.0.7
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-reshape2@1.4.5 r-readr@2.1.6 r-pbapply@1.7-4 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/mottensmann/GCalignR
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Simple Peak Alignment for Gas-Chromatography Data
Description:

Aligns peak based on peak retention times and matches homologous peaks across samples. The underlying alignment procedure comprises three sequential steps. (1) Full alignment of samples by linear transformation of retention times to maximise similarity among homologous peaks (2) Partial alignment of peaks within a user-defined retention time window to cluster homologous peaks (3) Merging rows that are likely representing homologous substances (i.e. no sample shows peaks in both rows and the rows have similar retention time means). The algorithm is described in detail in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.

r-ggquiver 0.4.0
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/mitchelloharawild/ggquiver
Licenses: GPL 3
Build system: r
Synopsis: Quiver Plots for 'ggplot2'
Description:

An extension of ggplot2 to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.

r-ggmix 0.0.2
Propagated dependencies: r-matrix@1.7-4 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: https://github.com/sahirbhatnagar/ggmix
Licenses: Expat
Build system: r
Synopsis: Variable Selection in Linear Mixed Models for SNP Data
Description:

Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.

r-glmmseq 0.5.7
Propagated dependencies: r-qvalue@2.42.0 r-plotly@4.11.0 r-pbapply@1.7-4 r-mcprogress@0.1.1 r-mass@7.3-65 r-lmertest@3.1-3 r-lme4@1.1-37 r-kableextra@1.4.0 r-glmmtmb@1.1.13 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-emmeans@2.0.0 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://myles-lewis.github.io/glmmSeq/
Licenses: Expat
Build system: r
Synopsis: General Linear Mixed Models for Gene-Level Differential Expression
Description:

Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.

r-gvlma 1.0.0.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gvlma
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Global Validation of Linear Models Assumptions
Description:

This package provides methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.

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Total results: 68655