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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-gisintegration 1.0
Propagated dependencies: r-tm@0.7-16 r-syn@0.1.0 r-stringr@1.6.0 r-shapefiles@0.7.2 r-sf@1.0-23 r-recordlinkage@0.4-12.6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GISINTEGRATION
Licenses: GPL 3
Build system: r
Synopsis: GIS Integration
Description:

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package GISINTEGRATION offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

r-glmmisrep 0.1.1
Propagated dependencies: r-poisson-glm-mix@1.4 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=glmMisrep
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Linear Models Adjusting for Misrepresentation
Description:

Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) <https://variancejournal.org/article/73151-maximum-likelihood-approaches-to-misrepresentation-models-in-glm-ratemaking-model-comparisons>.

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-gp 1.1
Propagated dependencies: r-rngforgpd@1.1.0 r-rfast@2.1.5.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gp
Licenses: GPL 2+
Build system: r
Synopsis: Maximum Likelihood Estimation of the Generalized Poisson Distribution
Description:

This package provides functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.

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-generalizedumatrixgpu 0.1.14
Dependencies: pandoc@2.19.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GeneralizedUmatrixGPU
Licenses: GPL 3
Build system: r
Synopsis: Credible Visualization for Two-Dimensional Projections of Data
Description:

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in Thrun, M.C. and Ultsch, A.: "Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods" (2020) <DOI:10.1016/j.mex.2020.101093>.

r-ggtrace 0.2.0
Dependencies: pandoc@2.19.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/rnabioco/ggtrace
Licenses: Expat
Build system: r
Synopsis: Trace and Highlight Groups of Data Points
Description:

This package provides ggplot2 geoms that allow groups of data points to be outlined or highlighted for emphasis. This is particularly useful when working with dense datasets that are prone to overplotting.

r-greekletters 1.0.4
Propagated dependencies: r-stringr@1.6.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=greekLetters
Licenses: GPL 2+
Build system: r
Synopsis: Routines for Writing Greek Letters and Mathematical Symbols on the 'RStudio' and 'RGui'
Description:

An implementation of functions to display Greek letters on the RStudio (include subscript and superscript indexes) and RGui (without subscripts and only with superscript 1, 2 or 3; because RGui doesn't support printing the corresponding Unicode characters as a string: all subscripts ranging from 0 to 9 and superscripts equal to 0, 4, 5, 6, 7, 8 or 9). The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the swirlify package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the clisymbols package.

r-graphon 0.3.6
Propagated dependencies: r-roptspace@0.2.4 r-rdpack@2.6.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=graphon
Licenses: Expat
Build system: r
Synopsis: Collection of Graphon Estimation Methods
Description:

This package provides a not-so-comprehensive list of methods for estimating graphon, a symmetric measurable function, from a single or multiple of observed networks. For a detailed introduction on graphon and popular estimation techniques, see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>. It also contains several auxiliary functions for generating sample networks using various network models and graphons.

r-geosae 0.1.0
Propagated dependencies: r-nlme@3.1-168 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/ketutdika/geoSAE
Licenses: GPL 3
Build system: r
Synopsis: Geoadditive Small Area Model
Description:

This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016)<doi:10.1002/9781118814963.ch13>, and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018)<doi:10.21082/jpptp.v2n2.2018.p101-110>.

r-ggcleveland 0.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/mpru/ggcleveland
Licenses: GPL 2
Build system: r
Synopsis: Implementation of Plots from Cleveland's Visualizing Data Book
Description:

William S. Cleveland's book Visualizing Data is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the ggplot2 versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis.

r-gggenomes 1.1.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://thackl.github.io/gggenomes/
Licenses: Expat
Build system: r
Synopsis: Grammar of Graphics for Comparative Genomics
Description:

An extension of ggplot2 for creating complex genomic maps. It builds on the power of ggplot2 and tidyverse adding new ggplot2'-style geoms & positions and dplyr'-style verbs to manipulate the underlying data. It implements a layout concept inspired by ggraph and introduces tracks to bring tidiness to the mess that is genomics data.

r-ggmulti 1.0.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=ggmulti
Licenses: GPL 2
Build system: r
Synopsis: High Dimensional Data Visualization
Description:

It provides materials (i.e. serial axes objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.

r-googleauthr 2.0.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://code.markedmondson.me/googleAuthR/
Licenses: Expat
Build system: r
Synopsis: Authenticate and Create Google APIs
Description:

Create R functions that interact with OAuth2 Google APIs <https://developers.google.com/apis-explorer/> easily, with auto-refresh and Shiny compatibility.

r-gamlss-foreach 1.1-6
Propagated dependencies: r-glmnet@4.1-10 r-gamlss-dist@6.1-1 r-gamlss-data@6.0-7 r-gamlss@5.5-0 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://www.gamlss.com/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Parallel Computations for Distributional Regression
Description:

Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.

r-gghalfnorm 1.1.2
Propagated dependencies: r-ggrepel@0.9.6 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/nathaneastwood/gghalfnorm
Licenses: AGPL 3
Build system: r
Synopsis: Create a Half Normal Plot Using 'ggplot2'
Description:

Reproduce the halfnorm() function found in the faraway package using the ggplot2 API.

r-gmoip 1.5.6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://relund.github.io/gMOIP/
Licenses: GPL 3+
Build system: r
Synopsis: Tools for 2D and 3D Plots of Single and Multi-Objective Linear/Integer Programming Models
Description:

Make 2D and 3D plots of linear programming (LP), integer linear programming (ILP), or mixed integer linear programming (MILP) models with up to three objectives. Plots of both the solution and criterion space are possible. For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models (see vignettes for an overview). The package also contains an function for checking if a point is inside the convex hull.

r-gemetrics 1.0.0
Propagated dependencies: r-bglr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GEmetrics
Licenses: GPL 3+
Build system: r
Synopsis: Best Linear Unbiased Prediction of Genotype-by-Environment Metrics
Description:

This package provides functions to calculate the best linear unbiased prediction of genotype-by-environment metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure, based on a multi-environment genomic prediction model.

r-gbop2 0.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GBOP2
Licenses: GPL 2
Build system: r
Synopsis: Generalized Bayesian Optimal Phase II Design (G-BOP2)
Description:

This package provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including: - PSO-Default, based on Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, "Particle Swarm Optimization"; - PSO-Quantum, based on Sun, Xu, and Feng (2004) <doi:10.1109/ICCIS.2004.1460396>, "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization"; - PSO-Dexp, based on Stehlà k et al. (2024) <doi:10.1016/j.asoc.2024.111913>, "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics"; - and PSO-GO.

r-geocacher 0.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=geocacheR
Licenses: GPL 3
Build system: r
Synopsis: Tools for Geocaching
Description:

This package provides tools for solving common geocaching puzzle types, and other Geocaching-related tasks.

r-googleimage2array 0.99.2
Propagated dependencies: r-xml2@1.5.0 r-rvest@1.0.5 r-magrittr@2.0.4 r-ebimage@4.52.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/kumeS/GoogleImage2Array
Licenses: Artistic License 2.0
Build system: r
Synopsis: Create Array Data from 2D Image Thumbnails via Google Image Search
Description:

Images are provided as an array dataset of 2D image thumbnails from Google Image Search <https://www.google.com/search>. This array data may be suitable for a training data of machine learning or deep learning as a first trial.

r-glmnetr 0.6-3
Propagated dependencies: r-xgboost@1.7.11.1 r-torch@0.16.3 r-survival@3.8-3 r-smoof@1.6.0.3 r-rpart@4.1.24 r-randomforestsrc@2.9.3 r-paramhelpers@1.14.2 r-mlrmbo@1.1.5.1 r-matrix@1.7-4 r-glmnet@4.1-10 r-aorsf@0.1.6
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmnetr
Licenses: GPL 3
Build system: r
Synopsis: Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models
Description:

Cross validation informed Relaxed LASSO (or more generally elastic net), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Oblique Random Forest ('aorsf'), Artificial Neural Network (ANN), Recursive Partitioning ('RPART') or step wise regression models are fit. Cross validation leave out samples (leading to nested cross validation) or bootstrap out-of-bag samples are used to evaluate and compare performances between these models with results presented in tabular or graphical means. Calibration plots can also be generated, again based upon (outer nested) cross validation or bootstrap leave out (out of bag) samples. Note, at the time of this writing, in order to fit gradient boosting machine models one must install the packages DiceKriging and rgenoud using the install.packages() function. For some datasets, for example when the design matrix is not of full rank, glmnet may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet(). This may be remedied by using the path=TRUE option when calling glmnet() and cv.glmnet(). Within the glmnetr package the approach of path=TRUE is taken by default. other packages doing similar include nestedcv <https://cran.r-project.org/package=nestedcv>, glmnetSE <https://cran.r-project.org/package=glmnetSE> which may provide different functionality when performing a nested CV. Use of the glmnetr has many similarities to the glmnet package and it could be helpful for the user of glmnetr also become familiar with the glmnet package <https://cran.r-project.org/package=glmnet>, with the "An Introduction to glmnet'" and "The Relaxed Lasso" being especially useful in this regard.

r-gremlin 1.1.0
Propagated dependencies: r-nlme@3.1-168 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/matthewwolak/gremlin
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Mixed-Effects REML Incorporating Generalized Inverses
Description:

Fit linear mixed-effects models using restricted (or residual) maximum likelihood (REML) and with generalized inverse matrices to specify covariance structures for random effects. In particular, the package is suited to fit quantitative genetic mixed models, often referred to as animal models'. Implements the average information algorithm as the main tool to maximize the restricted log-likelihood, but with other algorithms available.

r-genai 0.2.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://genai.gd.edu.kg/
Licenses: FSDG-compatible
Build system: r
Synopsis: Generative Artificial Intelligence
Description:

Utilizing Generative Artificial Intelligence models like GPT-4 and Gemini Pro as coding and writing assistants for R users. Through these models, GenAI offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid R users in streamlining laborious coding and language processing tasks.

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