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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-gdiff 0.2-5
Propagated dependencies: r-pdftools@3.6.0 r-magick@2.9.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/pmur002/
Licenses: GPL 2+
Build system: r
Synopsis: Graphical Difference Testing
Description:

This package provides functions for performing graphical difference testing. Differences are generated between raster images. Comparisons can be performed between different package versions and between different R versions.

r-greymodels 2.0.1
Propagated dependencies: r-shinywidgets@0.9.0 r-shinydashboard@0.7.3 r-shiny@1.11.1 r-scales@1.4.0 r-readxl@1.4.5 r-plotly@4.11.0 r-particle-swarm-optimisation@1.0.1 r-metrics@0.1.4 r-ggplot2@4.0.1 r-expm@1.0-0 r-dplyr@1.1.4 r-cmna@1.0.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/havishaJ/Greymodels
Licenses: GPL 3
Build system: r
Synopsis: Shiny App for Grey Forecasting Model
Description:

The Greymodels Shiny app is an interactive interface for statistical modelling and forecasting using grey-based models. It covers several state-of-the-art univariate and multivariate grey models. A user friendly interface allows users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within user chosen confidence intervals. Chang, C. (2019) <doi:10.24818/18423264/53.1.19.11>, Li, K., Zhang, T. (2019) <doi:10.1007/s12667-019-00344-0>, Ou, S. (2012) <doi:10.1016/j.compag.2012.03.007>, Li, S., Zhou, M., Meng, W., Zhou, W. (2019) <doi:10.1080/23307706.2019.1666310>, Xie, N., Liu, S. (2009) <doi:10.1016/j.apm.2008.01.011>, Shao, Y., Su, H. (2012) <doi:10.1016/j.aasri.2012.06.003>, Xie, N., Liu, S., Yang, Y., Yuan, C. (2013) <doi:10.1016/j.apm.2012.10.037>, Li, S., Miao, Y., Li, G., Ikram, M. (2020) <doi:10.1016/j.matcom.2019.12.020>, Che, X., Luo, Y., He, Z. (2013) <doi:10.4028/www.scientific.net/AMM.364.207>, Zhu, J., Xu, Y., Leng, H., Tang, H., Gong, H., Zhang, Z. (2016) <doi:10.1109/appeec.2016.7779929>, Luo, Y., Liao, D. (2012) <doi:10.4028/www.scientific.net/AMR.507.265>, Bilgil, H. (2020) <doi:10.3934/math.2021091>, Li, D., Chang, C., Chen, W., Chen, C. (2011) <doi:10.1016/j.apm.2011.04.006>, Chen, C. (2008) <doi:10.1016/j.chaos.2006.08.024>, Zhou, W., Pei, L. (2020) <doi:10.1007/s00500-019-04248-0>, Xiao, X., Duan, H. (2020) <doi:10.1016/j.engappai.2019.103350>, Xu, N., Dang, Y. (2015) <doi:10.1155/2015/606707>, Chen, P., Yu, H.(2014) <doi:10.1155/2014/242809>, Zeng, B., Li, S., Meng, W., Zhang, D. (2019) <doi:10.1371/journal.pone.0221333>, Liu, L., Wu, L. (2021) <doi:10.1016/j.apm.2020.08.080>, Hu, Y. (2020) <doi:10.1007/s00500-020-04765-3>, Zhou, P., Ang, B., Poh, K. (2006) <doi:10.1016/j.energy.2005.12.002>, Cheng, M., Li, J., Liu, Y., Liu, B. (2020) <doi:10.3390/su12020698>, Wang, H., Wang, P., Senel, M., Li, T. (2019) <doi:10.1155/2019/9049815>, Ding, S., Li, R. (2020) <doi:10.1155/2020/4564653>, Zeng, B., Li, C. (2018) <doi:10.1016/j.cie.2018.02.042>, Xie, N., Liu, S. (2015) <doi:10.1109/JSEE.2015.00013>, Zeng, X., Yan, S., He, F., Shi, Y. (2019) <doi:10.1016/j.apm.2019.11.032>.

r-glm-predict 4.3-2
Propagated dependencies: r-vgam@1.1-13 r-survival@3.8-3 r-survey@4.4-8 r-nnet@7.3-20 r-mlogit@1.1-3 r-mass@7.3-65 r-lme4@1.1-37 r-dfidx@0.2-0 r-aer@1.2-15
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/benjaminschlegel/glm.predict/
Licenses: GPL 2+
Build system: r
Synopsis: Predicted Values and Discrete Changes for Regression Models
Description:

This package provides functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model], vglm() [generalized ordinal logistic model], multinom() [multinomial model], tobit() [tobit model], svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.

r-glmmep 1.0-3.1
Propagated dependencies: r-matrixcalc@1.0-6 r-lme4@1.1-37
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmmEP
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Linear Mixed Model Analysis via Expectation Propagation
Description:

Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.

r-gsmeanfreq 0.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gsMeanFreq
Licenses: GPL 3
Build system: r
Synopsis: Group Sequential Clinical Trial Designs for Composite Endpoints
Description:

Simulating composite endpoints with recurrent and terminal events under staggered entry, and for constructing one- and two-sample group sequential test statistics and monitoring boundaries based on the mean frequency function. Details will be available in an upcoming publication.

r-groupseq 1.4.3
Propagated dependencies: r-tcltk2@1.6.1 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://rpahl.github.io/GroupSeq/
Licenses: GPL 3
Build system: r
Synopsis: Group Sequential Design Probabilities - With Graphical User Interface
Description:

Computes probabilities related to group sequential designs for normally distributed test statistics. Enables to derive critical boundaries, power, drift, and confidence intervals of such designs. Supports the alpha spending approach by Lan-DeMets (1994) <doi:10.1002/sim.4780131308>.

r-gamlr 1.13-9
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', <doi:10.48550/arXiv.1308.5623>.

r-gpairs 1.4.0
Propagated dependencies: r-vcd@1.4-13 r-mass@7.3-65 r-lattice@0.22-7 r-colorspace@2.1-2 r-barcode@1.4.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gpairs
Licenses: GPL 2+
Build system: r
Synopsis: The Generalized Pairs Plot
Description:

Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.

r-gents 0.1.4
Propagated dependencies: r-shiny@1.11.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=genTS
Licenses: Expat
Build system: r
Synopsis: R Shiny App for Creating Simplified Trial Summary (TS) Domain
Description:

Make it easy to create simplified trial summary (TS) domain based on FDA FDA guide <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.

r-gomms 1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gomms
Licenses: GPL 2+
Build system: r
Synopsis: GLM-Based Ordination Method
Description:

This package provides a zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.

r-glorenz 0.1.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glorenz
Licenses: GPL 3
Build system: r
Synopsis: Transformed and Relative Lorenz Curves for Survey Weighted Data
Description:

This package provides functions for constructing Transformed and Relative Lorenz curves with survey sampling weights. Given a variable of interest measured in two groups with scaled survey weights so that their hypothetical populations are of equal size, tlorenz() computes the proportion of members of the group with smaller values (ordered from smallest to largest) needed for their sum to match the sum of the top qth percentile of the group with higher values. rlorenz() shows the fraction of the total value of the group with larger values held by the pth percentile of those in the group with smaller values. Fd() is a survey weighted cumulative distribution function and Eps() is a survey weighted inverse cdf used in rlorenz(). Ramos, Graubard, and Gastwirth (2025) <doi:10.1093/jrsssa/qnaf044>.

r-ggrain 0.1.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/njudd/ggrain
Licenses: Expat
Build system: r
Synopsis: Rainclouds Geom for 'ggplot2'
Description:

The geom_rain() function adds different geoms together using ggplot2 to create raincloud plots.

r-gawdis 0.1.5
Propagated dependencies: r-ga@3.2.4 r-fd@1.0-12.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/pavel-fibich/gawdis/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Multi-Trait Dissimilarity with more Uniform Contributions
Description:

R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.

r-gicf 1.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gicf
Licenses: GPL 3
Build system: r
Synopsis: Penalised Likelihood Estimation of a Covariance Matrix
Description:

Penalised likelihood estimation of a covariance matrix via the ridge-regularised covglasso estimator described in Cibinel et al. (2024) <doi:10.48550/arXiv.2410.02403>. Based on the C++ code of the R package covglasso (by Michael Fop, <https://orcid.org/0000-0003-3936-2757>) and the R code of icf (by Mathias Drton, <https://orcid.org/0000-0001-5614-3025>) within the R package ggm'.

r-gadag 0.99.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GADAG
Licenses: GPL 2
Build system: r
Synopsis: Genetic Algorithm for Learning Directed Acyclic Graphs
Description:

Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).

r-gocompare 1.0.2.2
Propagated dependencies: r-vegan@2.7-2 r-stringr@1.6.0 r-mathjaxr@1.8-0 r-igraph@2.2.1 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/ccsosa/GOCompare
Licenses: GPL 3+
Build system: r
Synopsis: Comprehensive GO Terms Comparison Between Species
Description:

Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.

r-gendist 2.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gendist
Licenses: GPL 2+
Build system: r
Synopsis: Generated Probability Distribution Models
Description:

Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.

r-gglorenz 0.0.2
Propagated dependencies: r-ineq@0.2-13 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/jjchern/gglorenz
Licenses: Expat
Build system: r
Synopsis: Plotting Lorenz Curve with the Blessing of 'ggplot2'
Description:

This package provides statistical transformations for plotting empirical ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.

r-ggplotgui 1.0.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/gertstulp/ggplotgui/
Licenses: GPL 3
Build system: r
Synopsis: Create Ggplots via a Graphical User Interface
Description:

Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.

r-garma 1.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/rlph50/garma
Licenses: GPL 3
Build system: r
Synopsis: Fitting and Forecasting Gegenbauer ARMA Time Series Models
Description:

This package provides methods for estimating univariate long memory-seasonal/cyclical Gegenbauer time series processes. See for example (2022) <doi:10.1007/s00362-022-01290-3>. Refer to the vignette for details of fitting these processes.

r-gmdh 1.6
Propagated dependencies: 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=GMDH
Licenses: GPL 2+
Build system: r
Synopsis: Short Term Forecasting via GMDH-Type Neural Network Algorithms
Description:

Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.

r-glmmlasso 1.6.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=glmmLasso
Licenses: GPL 2
Build system: r
Synopsis: Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation
Description:

This package provides a variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>. See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.

r-geostatsp 2.0.11
Propagated dependencies: r-terra@1.8-86 r-numderiv@2016.8-1.1 r-matrix@1.7-4 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=geostatsp
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Geostatistical Modelling with Likelihood and Bayes
Description:

Geostatistical modelling facilities using SpatRaster and SpatVector objects are provided. Non-Gaussian models are fit using INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The RandomFields package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.

r-glmmfields 0.1.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/seananderson/glmmfields
Licenses: GPL 3+
Build system: r
Synopsis: Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling
Description:

This package implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. glmmfields uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with Stan'. References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.

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Total results: 69112