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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-groupedsurv 1.0.5.1
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-qvalue@2.42.0 r-foreach@1.5.2 r-doparallel@1.0.17 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=groupedSurv
Licenses: GPL 2+
Build system: r
Synopsis: Efficient Estimation of Grouped Survival Models Using the Exact Likelihood Function
Description:

These Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the Cuba library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).

r-gemtc 1.1-0
Propagated dependencies: r-truncnorm@1.0-9 r-rjags@4-17 r-rglpk@0.6-5.1 r-plyr@1.8.9 r-meta@8.2-1 r-igraph@2.2.1 r-forcats@1.0.1 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/gertvv/gemtc
Licenses: GPL 3
Build system: r
Synopsis: Network Meta-Analysis Using Bayesian Methods
Description:

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.

r-gentwoarmstrialsize 0.0.5
Propagated dependencies: r-trialsize@1.4.1 r-hmisc@5.2-4 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=GenTwoArmsTrialSize
Licenses: GPL 3
Build system: r
Synopsis: Generalized Two Arms Clinical Trial Sample Size Calculation
Description:

Two arms clinical trials required sample size is calculated in the comprehensive parametric context. The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) <doi:10.1201/9781315183084>, Wittes, J (2002) <doi:10.1093/epirev/24.1.39>, Sato, T (2000) <doi:10.1002/1097-0258(20001015)19:19%3C2689::aid-sim555%3E3.0.co;2-0>, Lachin J M, Foulkes, M A (1986) <doi:10.2307/2531201>, Whitehead J(1993) <doi:10.1002/sim.4780122404>, Julious SA (2023) <doi:10.1201/9780429503658>.

r-gimmegvar 0.1.0
Propagated dependencies: r-qgraph@1.9.8 r-png@0.1-8 r-here@1.0.2 r-graphicalvar@0.3.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GIMMEgVAR
Licenses: GPL 2
Build system: r
Synopsis: Group Iterative Multiple Model Estimation with 'graphicalVAR'
Description:

Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the graphicalVAR approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

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-ggalign 1.2.0
Propagated dependencies: r-vctrs@0.6.5 r-scales@1.4.0 r-s7@0.2.1 r-rlang@1.1.6 r-lifecycle@1.0.4 r-gtable@0.3.6 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/Yunuuuu/ggalign
Licenses: Expat
Build system: r
Synopsis: 'ggplot2' Extension for Composable Visualization
Description:

This package provides a ggplot2 extension providing an integrative framework for composable visualization, enabling the creation of complex multi-plot layouts such as insets, circular arrangements, and multi-panel compositions. Built on the grammar of graphics, it offers tools to align, stack, and nest plots, simplifying the construction of richly annotated figures for high-dimensional data contextsâ such as genomics, transcriptomics, and microbiome studiesâ by making it easy to link related plots, overlay clustering results, or highlight shared patterns.

r-gmcplite 0.1.6
Propagated dependencies: r-mvtnorm@1.3-3 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://merck.github.io/gMCPLite/
Licenses: GPL 3
Build system: r
Synopsis: Lightweight Graph Based Multiple Comparison Procedures
Description:

This package provides a lightweight fork of gMCP with functions for graphical described multiple test procedures introduced in Bretz et al. (2009) <doi:10.1002/sim.3495> and Bretz et al. (2011) <doi:10.1002/bimj.201000239>. Implements a flexible function using ggplot2 to create multiplicity graph visualizations. Contains instructions of multiplicity graph and graphical testing for group sequential design, described in Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>, with necessary unit testing using testthat'.

r-gofkernel 2.1-3
Propagated dependencies: 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=GoFKernel
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Testing Goodness-of-Fit with the Kernel Density Estimator
Description:

Tests of goodness-of-fit based on a kernel smoothing of the data. References: Pavà a (2015) <doi:10.18637/jss.v066.c01>.

r-ggokabeito 0.1.0
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/malcolmbarrett/ggokabeito
Licenses: Expat
Build system: r
Synopsis: 'Okabe-Ito' Scales for 'ggplot2' and 'ggraph'
Description:

Discrete scales for the colorblind-friendly Okabe-Ito palette, including color', fill', and edge_colour'. ggokabeito provides ggplot2 and ggraph scales to easily use the Okabe-Ito palette in your data visualizations.

r-gsmams 0.7.2
Propagated dependencies: r-survival@3.8-3 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/Tpatni719/gsMAMS
Licenses: GPL 3
Build system: r
Synopsis: Group Sequential Designs of Multi-Arm Multi-Stage Trials
Description:

It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).

r-gravitas 0.1.3
Propagated dependencies: r-tsibble@1.1.6 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-shiny@1.11.1 r-rlang@1.1.6 r-rcolorbrewer@1.1-3 r-magrittr@2.0.4 r-lvplot@0.2.2 r-lubridate@1.9.4 r-ineq@0.2-13 r-ggridges@0.5.7 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/Sayani07/gravitas/
Licenses: GPL 3
Build system: r
Synopsis: Explore Probability Distributions for Bivariate Temporal Granularities
Description:

This package provides tools for systematically exploring large quantities of temporal data across cyclic temporal granularities (deconstructions of time) by visualizing probability distributions. Cyclic time granularities can be circular, quasi-circular or aperiodic. gravitas computes cyclic single-order-up or multiple-order-up granularities, check the feasibility of creating plots for any two cyclic granularities and recommend probability distributions plots for exploring periodicity in the data.

r-gsd 1.0.0
Propagated dependencies: r-matrix@1.7-4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-ebayesthresh@1.4-12
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GSD
Licenses: GPL 2+
Build system: r
Synopsis: Graph Signal Decomposition
Description:

Graph signals residing on the vertices of a graph have recently gained prominence in research in various fields. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. Recently, several notable graph signal decomposition methods have been proposed, which include graph Fourier decomposition based on graph Fourier transform, graph empirical mode decomposition, and statistical graph empirical mode decomposition. This package efficiently implements multiscale analysis applicable to various fields, and offers an effective tool for visualizing and decomposing graph signals. For the detailed methodology, see Ortega et al. (2018) <doi:10.1109/JPROC.2018.2820126>, Shuman et al. (2013) <doi:10.1109/MSP.2012.2235192>, Tremblay et al. (2014) <https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf>, and Cho et al. (2024) "Statistical graph empirical mode decomposition by graph denoising and boundary treatment".

r-ggvfields 1.0.0
Propagated dependencies: r-tibble@3.3.0 r-scales@1.4.0 r-numderiv@2016.8-1.1 r-ggplot2@4.0.1 r-farver@2.1.2 r-desolve@1.40 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/dusty-turner/ggvfields
Licenses: Expat
Build system: r
Synopsis: Vector Field Visualizations with 'ggplot2'
Description:

This package provides a ggplot2 extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns.

r-gpltr 1.5
Propagated dependencies: r-rpart@4.1.24
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GPLTR
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Partially Linear Tree-Based Regression Model
Description:

Combining a generalized linear model with an additional tree part on the same scale. A four-step procedure is proposed to fit the model and test the joint effect of the selected tree part while adjusting on confounding factors. We also proposed an ensemble procedure based on the bagging to improve prediction accuracy and computed several scores of importance for variable selection. See Cyprien Mbogning et al.'(2014)<doi:10.1186/2043-9113-4-6> and Cyprien Mbogning et al.'(2015)<doi:10.1159/000380850> for an overview of all the methods implemented in this package.

r-gms 0.31.2
Propagated dependencies: r-yaml@2.3.10 r-withr@3.0.2 r-stringr@1.6.0 r-rlang@1.1.6 r-filelock@1.0.3 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/pik-piam/gms
Licenses: FreeBSD
Build system: r
Synopsis: 'GAMS' Modularization Support Package
Description:

This package provides a collection of tools to create, use and maintain modularized model code written in the modeling language GAMS (<https://www.gams.com/>). Out-of-the-box GAMS does not come with support for modularized model code. This package provides the tools necessary to convert a standard GAMS model to a modularized one by introducing a modularized code structure together with a naming convention which emulates local environments. In addition, this package provides tools to monitor the compliance of the model code with modular coding guidelines.

r-gotop 0.1.4
Propagated dependencies: r-jsonlite@2.0.0 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://felixluginbuhl.com/gotop/
Licenses: Expat
Build system: r
Synopsis: Scroll Back to Top Icon in Shiny and R Markdown
Description:

Add a scroll back to top Font Awesome icon <https://fontawesome.com/> in rmarkdown documents and shiny apps thanks to jQuery GoTop <https://scottdorman.blog/jquery-gotop/>.

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-gptstudio 0.4.0
Propagated dependencies: r-yaml@2.3.10 r-waiter@0.2.5-1.927501b r-stringr@1.6.0 r-sseparser@0.1.0 r-shiny-i18n@0.3.0 r-shiny@1.11.1 r-rvest@1.0.5 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-ids@1.0.1 r-httr2@1.2.1 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-glue@1.8.0 r-fontawesome@0.5.3 r-curl@7.0.0 r-colorspace@2.1-2 r-cli@3.6.5 r-bslib@0.9.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/MichelNivard/gptstudio
Licenses: Expat
Build system: r
Synopsis: Use Large Language Models Directly in your Development Environment
Description:

Large language models are readily accessible via API. This package lowers the barrier to use the API inside of your development environment. For more on the API, see <https://platform.openai.com/docs/introduction>.

r-gdalutilities 1.2.5
Propagated dependencies: r-sf@1.0-23
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/JoshOBrien/gdalUtilities/
Licenses: GPL 2+
Build system: r
Synopsis: Wrappers for 'GDAL' Utilities Executables
Description:

R's sf package ships with self-contained GDAL executables, including a bare bones interface to several GDAL'-related utility programs collectively known as the GDAL utilities'. For each of those utilities, this package provides an R wrapper whose formal arguments closely mirror those of the GDAL command line interface. The utilities operate on data stored in files and typically write their output to other files. Therefore, to process data stored in any of R's more common spatial formats (i.e. those supported by the sf and terra packages), first write them to disk, then process them with the package's wrapper functions before reading the outputted results back into R. GDAL function arguments introduced in GDAL version 3.5.2 or earlier are supported.

r-gamair 1.0-2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gamair
Licenses: GPL 2+
Build system: r
Synopsis: Data for 'GAMs: An Introduction with R'
Description:

Data sets and scripts used in the book Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.

r-gmgeostats 0.11.4
Propagated dependencies: r-sp@2.2-0 r-rcolorbrewer@1.1-3 r-gstat@2.1-4 r-foreach@1.5.2 r-compositions@2.0-9 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://codebase.helmholtz.cloud/geomet/gmGeostats
Licenses: CC-BY-SA 4.0 GPL 2+
Build system: r
Synopsis: Geostatistics for Compositional Analysis
Description:

Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.

r-gremlins 0.2.1
Propagated dependencies: r-r6@2.6.1 r-pbmcapply@1.5.1 r-igraph@2.2.1 r-blockmodels@1.1.5 r-aricode@1.0.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://GrossSBM.github.io/GREMLINS/
Licenses: GPL 3
Build system: r
Synopsis: Generalized Multipartite Networks
Description:

We define generalized multipartite networks as the joint observation of several networks implying some common pre-specified groups of individuals. The aim is to fit an adapted version of the popular stochastic block model to multipartite networks, as described in Bar-hen, Barbillon and Donnet (2020) <arXiv:1807.10138>.

r-govinfor 0.0.3
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-lubridate@1.9.4 r-janitor@2.2.1 r-httr2@1.2.1 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/blackerby/govinfoR
Licenses: Expat
Build system: r
Synopsis: 'GovInfo' API Wrapper
Description:

Access data provided by the United States Government Publishing Office (GPO) GovInfo API (<https://github.com/usgpo/api>).

r-growthrates 0.8.5
Propagated dependencies: r-lattice@0.22-7 r-fme@1.3.6.4 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/tpetzoldt/growthrates
Licenses: GPL 2+
Build system: r
Synopsis: Estimate Growth Rates from Experimental Data
Description:

This package provides a collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.

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Total results: 68658