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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-bayesrs 0.1.3
Propagated dependencies: r-rjags@4-17 r-reshape@0.8.10 r-metrology@0.9-29-2 r-ggplot2@4.0.1 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesRS
Licenses: GPL 2+
Build system: r
Synopsis: Bayes Factors for Hierarchical Linear Models with Continuous Predictors
Description:

Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).

r-barcodingr 1.0-3
Propagated dependencies: r-sp@2.2-0 r-nnet@7.3-20 r-class@7.3-23 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BarcodingR
Licenses: GPL 2
Build system: r
Synopsis: Species Identification using DNA Barcodes
Description:

To perform species identification using DNA barcodes.

r-bootes 1.3.1
Propagated dependencies: r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/dgerlanc/bootES
Licenses: GPL 2+
Build system: r
Synopsis: Bootstrap Confidence Intervals on Effect Sizes
Description:

Calculate robust measures of effect sizes using the bootstrap.

r-bibplots 0.0.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BibPlots
Licenses: FSDG-compatible
Build system: r
Synopsis: Plot Functions for Use in Bibliometrics
Description:

Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software CRExplorer (see <http://crexplorer.net>). Further extension to more plot variants is planned.

r-beaver 1.0.0
Propagated dependencies: r-yodel@1.0.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-rjags@4-17 r-purrr@1.2.0 r-ggplot2@4.0.1 r-fs@1.6.6 r-ellipsis@0.3.2 r-dplyr@1.1.4 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/rich-payne/beaver
Licenses: Expat
Build system: r
Synopsis: Bayesian Model Averaging of Covariate Adjusted Negative-Binomial Dose-Response
Description:

Dose-response modeling for negative-binomial distributed data with a variety of dose-response models. Covariate adjustment and Bayesian model averaging is supported. Functions are provided to easily obtain inference on the dose-response relationship and plot the dose-response curve.

r-bdrc 1.1.0
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-gridextra@2.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bdrc
Licenses: Expat
Build system: r
Synopsis: Bayesian Discharge Rating Curves
Description:

Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>.

r-betasandwich 1.0.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jeksterslab/betaSandwich
Licenses: Expat
Build system: r
Synopsis: Robust Confidence Intervals for Standardized Regression Coefficients
Description:

Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

r-bidag 2.1.4
Propagated dependencies: r-rgraphviz@2.54.0 r-rcpp@1.1.0 r-rbgl@1.86.0 r-pcalg@2.7-12 r-matrix@1.7-4 r-graph@1.88.0 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BiDAG
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Inference for Directed Acyclic Graphs
Description:

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

r-benthos 1.3-9
Propagated dependencies: r-readr@2.1.6 r-lazyeval@0.2.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=benthos
Licenses: GPL 3+
Build system: r
Synopsis: Marine Benthic Ecosystem Analysis
Description:

Preprocessing tools and biodiversity measures (species abundance, species richness, population heterogeneity and sensitivity) for analysing marine benthic data. See Van Loon et al. (2015) <doi:10.1016/j.seares.2015.05.002> for an application of these tools.

r-bioi 0.2.10
Propagated dependencies: r-rcpp@1.1.0 r-igraph@2.2.1 r-dplyr@1.1.4 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=Bioi
Licenses: GPL 3
Build system: r
Synopsis: Biological Image Analysis
Description:

Single linkage clustering and connected component analyses are often performed on biological images. Bioi provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions.

r-blockmatrix 1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://cri.gmpf.eu/Research/Sustainable-Agro-Ecosystems-and-Bioresources/Dynamics-in-the-agro-ecosystems/people/Emanuele-Cordano
Licenses: GPL 2+
Build system: r
Synopsis: blockmatrix: Tools to solve algebraic systems with partitioned matrices
Description:

Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed.

r-btb 0.2.2
Propagated dependencies: r-sf@1.0-23 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mapsf@1.1.0 r-magrittr@2.0.4 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/InseeFr/btb
Licenses: GPL 2+
Build system: r
Synopsis: Beyond the Border - Kernel Density Estimation for Urban Geography
Description:

The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.

r-boxr 0.3.7
Propagated dependencies: r-withr@3.0.2 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-rio@1.2.4 r-purrr@1.2.0 r-mime@0.13 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-jsonlite@2.0.0 r-jose@1.2.1 r-httr@1.4.7 r-glue@1.8.0 r-fs@1.6.6 r-dplyr@1.1.4 r-digest@0.6.39 r-cli@3.6.5 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://r-box.github.io/boxr/
Licenses: Expat
Build system: r
Synopsis: Interface for the 'Box.com API'
Description:

An R interface for the remote file hosting service Box (<https://www.box.com/>). In addition to uploading and downloading files, this package includes functions which mirror base R operations for local files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well as git style functions for entire directories (e.g. box_fetch(), box_push()).

r-bigsparser 0.7.3
Propagated dependencies: r-rmio@0.4.0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4 r-bigassertr@0.1.7
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/privefl/bigsparser
Licenses: GPL 3
Build system: r
Synopsis: Sparse Matrix Format with Data on Disk
Description:

Provide a sparse matrix format with data stored on disk, to be used in both R and C++. This is intended for more efficient use of sparse data in C++ and also when parallelizing, since data on disk does not need copying. Only a limited number of features will be implemented. For now, conversion can be performed from a dgCMatrix or a dsCMatrix from R package Matrix'. A new compact format is also now available.

r-bspmma 0.1-2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bspmma
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Semiparametric Models for Meta-Analysis
Description:

The main functions carry out Gibbs sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis.

r-bivpois 1.1
Propagated dependencies: r-rfast@2.1.5.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bivpois
Licenses: GPL 2+
Build system: r
Synopsis: Bivariate Poisson Distribution
Description:

Maximum likelihood estimation, random values generation, density computation and other functions for the bivariate Poisson distribution. References include: Kawamura K. (1984). "Direct calculation of maximum likelihood estimator for the bivariate Poisson distribution". Kodai Mathematical Journal, 7(2): 211--221. <doi:10.2996/kmj/1138036908>. Kocherlakota S. and Kocherlakota K. (1992). "Bivariate discrete distributions". CRC Press. <doi:10.1201/9781315138480>. Karlis D. and Ntzoufras I. (2003). "Analysis of sports data by using bivariate Poisson models". Journal of the Royal Statistical Society: Series D (The Statistician), 52(3): 381--393. <doi:10.1111/1467-9884.00366>.

r-bar 0.1.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BAR
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Adaptive Randomization
Description:

Bayesian adaptive randomization is also called outcome adaptive randomization, which is increasingly used in clinical trials.

r-bootsurv 0.0.1
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bootsurv
Licenses: GPL 3
Build system: r
Synopsis: Bootstrap Methods for Complete Survey Data
Description:

Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis.

r-binomci 1.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=binomCI
Licenses: GPL 2+
Build system: r
Synopsis: Confidence Intervals for a Binomial Proportion
Description:

Twelve confidence intervals for one binomial proportion or a vector of binomial proportions are computed. The confidence intervals are: Jeffreys, Wald, Wald corrected, Wald, Blyth and Still, Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. <doi:10.1002/sim.4780120902>.

r-bnviewer 0.1.6
Propagated dependencies: r-visnetwork@2.1.4 r-shiny@1.11.1 r-igraph@2.2.1 r-e1071@1.7-16 r-caret@7.0-1 r-bnlearn@5.1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://robsonfernandes.net/bnviewer/
Licenses: Expat
Build system: r
Synopsis: Bayesian Networks Interactive Visualization and Explainable Artificial Intelligence
Description:

Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The bnviewer is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages.

r-bayesgrowth 1.0.0
Propagated dependencies: r-tidybayes@3.0.7 r-tibble@3.3.0 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-loo@2.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bh@1.87.0-1 r-bayesplot@1.14.0 r-aquaticlifehistory@1.0.6
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jonathansmart/BayesGrowth
Licenses: GPL 3
Build system: r
Synopsis: Estimate Fish Growth Using MCMC Analysis
Description:

Estimate fish length-at-age models using MCMC analysis with rstan models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 <doi:10.1371/journal.pone.0246734>.

r-binb 0.0.7
Propagated dependencies: r-rmarkdown@2.30 r-knitr@1.50 r-codetools@0.2-20
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/eddelbuettel/binb
Licenses: GPL 2+
Build system: r
Synopsis: 'binb' is not 'Beamer'
Description:

This package provides a collection of LaTeX styles using Beamer customization for pdf-based presentation slides in RMarkdown'. At present it contains RMarkdown adaptations of the LaTeX themes Metropolis (formerly mtheme') theme by Matthias Vogelgesang and others (now included in TeXLive'), the IQSS by Ista Zahn (which is included here), and the Monash theme by Rob J Hyndman. Additional (free) fonts may be needed: Metropolis prefers Fira', and IQSS requires Libertinus'.

r-bayesmultimode 0.7.4
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-sn@2.1.1 r-rdpack@2.6.4 r-posterior@1.6.1 r-mvtnorm@1.3-3 r-mcmcglmm@2.36 r-magrittr@2.0.4 r-gtools@3.9.5 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bayesplot@1.14.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/paullabonne/BayesMultiMode
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Mode Inference
Description:

This package provides a two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>). First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference.

r-bayesmeanscale 0.2.1
Propagated dependencies: r-posterior@1.6.1 r-magrittr@2.0.4 r-data-table@1.17.8 r-bayestestr@0.17.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/dalenbe2/bayesMeanScale
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Post-Estimation on the Mean Scale
Description:

Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.

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Total results: 68658