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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-bagoft 1.0.0
Propagated dependencies: r-randomforest@4.7-1.2 r-dcov@0.1.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BAGofT
Licenses: GPL 3
Build system: r
Synopsis: Binary Regression Adaptive Goodness-of-Fit Test (BAGofT)
Description:

The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.

r-bravo 3.2.2
Propagated dependencies: r-rcpp@1.1.0 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bravo
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Screening and Variable Selection
Description:

This package performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.

r-blvim 0.1.1
Propagated dependencies: r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-collapse@2.1.5 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=blvim
Licenses: GPL 3+
Build system: r
Synopsis: Boltzmann–Lotka–Volterra Interaction Model
Description:

Estimates Boltzmannâ Lotkaâ Volterra (BLV) interaction model efficiently. Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) <dx.doi.org/10.1098/rsif.2007.1288>.

r-braidrm 1.0.6
Propagated dependencies: r-basicdrm@0.3.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=braidrm
Licenses: GPL 3+
Build system: r
Synopsis: Fitting Combined Action with the BRAID Response Surface Model
Description:

This package contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Doses (BRAID) model of combined action, along with tools for implementing other combination analysis methods, including Bliss independence, combination index, and additional response surface methods.

r-biostatr 4.1.1
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://fbertran.github.io/BioStatR/
Licenses: GPL 3
Build system: r
Synopsis: Initiation à La Statistique Avec R
Description:

Datasets and functions for the book "Initiation à la Statistique avec R", F. Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth edition).

r-bayesdp 1.3.7
Propagated dependencies: r-survival@3.8-3 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mcmcpack@1.7-1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/graemeleehickey/bayesDP
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Implementation of the Bayesian Discount Prior Approach for Clinical Trials
Description:

This package provides functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.

r-bivgeo 2.1.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://doi.org/10.1285/i20705948v11n1p108
Licenses: GPL 2+
Build system: r
Synopsis: Basu-Dhar Bivariate Geometric Distribution
Description:

This package provides functions to compute the joint probability mass function (pmf), cumulative distribution function (cdf), and survival function (sf) of the Basu-Dhar bivariate geometric distribution. Additional functionalities include the calculation of the correlation coefficient, covariance, and cross-factorial moments, as well as the generation of random variates. The package also implements parameter estimation based on the method of moments.

r-blendr 1.0.0
Propagated dependencies: r-tibble@3.3.0 r-survhe@2.0.51 r-sn@2.1.1 r-manipulate@1.0.1 r-ggplot2@4.0.1 r-flexsurv@2.3.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/StatisticsHealthEconomics/blendR/
Licenses: GPL 3+
Build system: r
Synopsis: Blended Survival Curves
Description:

Create a blended curve from two survival curves, which is particularly useful for survival extrapolation in health technology assessment. The main idea is to mix a flexible model that fits the observed data well with a parametric model that encodes assumptions about long-term survival. The two curves are blended into a single survival curve that is identical to the first model over the range of observed times and gradually approaches the parametric model over the extrapolation period based on a given weight function. This approach allows for the inclusion of external information, such as data from registries or expert opinion, to guide long-term extrapolations, especially when dealing with immature trial data. See Che et al. (2022) <doi:10.1177/0272989X221134545>.

r-bayespower 1.0.1
Propagated dependencies: r-shinywidgets@0.9.0 r-shiny@1.11.1 r-rootsolve@1.8.2.4 r-rmarkdown@2.30 r-rlang@1.1.6 r-rcpp@1.1.0 r-patchwork@1.3.2 r-hypergeo@1.2-14 r-gsl@2.1-9 r-glue@1.8.0 r-ggplot2@4.0.1 r-extdist@0.7-4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesPower
Licenses: GPL 3+
Build system: r
Synopsis: Sample Size and Power Calculation for Bayesian Testing with Bayes Factor
Description:

The goal of BayesPower is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive shiny application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) <doi:10.31234/osf.io/pgdac_v1>.

r-bcv 1.0.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/michbur/bcv
Licenses: Modified BSD
Build system: r
Synopsis: Cross-Validation for the SVD (Bi-Cross-Validation)
Description:

This package provides methods for choosing the rank of an SVD (singular value decomposition) approximation via cross validation. The package provides both Gabriel-style "block" holdouts and Wold-style "speckled" holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry's 2009 AoAS article (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis (at <arXiv:0909.3052>).

r-binarybalancedcut 0.2
Propagated dependencies: r-reshape2@1.4.5 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=BinarybalancedCut
Licenses: GPL 2
Build system: r
Synopsis: Threshold Cut Point of Probability for a Binary Classifier Model
Description:

Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.

r-bartman 0.2.1
Propagated dependencies: r-tidyr@1.3.1 r-tidygraph@1.3.1 r-scales@1.4.0 r-rrapply@1.2.8 r-rlang@1.1.6 r-rjava@1.0-11 r-purrr@1.2.0 r-patchwork@1.3.2 r-gtable@0.3.6 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-ggiraph@0.9.2 r-dplyr@1.1.4 r-dendser@1.0.2 r-dbarts@0.9-32 r-cowplot@1.2.0 r-colorspace@2.1-2 r-bartmachine@1.4.1.1 r-bart@2.9.10
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bartMan
Licenses: GPL 2+
Build system: r
Synopsis: Create Visualisations for BART Models
Description:

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits. We construct conventional plots to analyze a modelâ s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely BART Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>, dbarts (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>, and bartMachine (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.

r-bgvar 2.5.9
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-stochvol@3.2.8 r-readxl@1.4.5 r-rcppprogress@0.4.2 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-mass@7.3-65 r-knitr@1.50 r-gigrvg@0.8 r-coda@0.19-4.1 r-bayesm@3.1-7 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/mboeck11/BGVAR
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Global Vector Autoregressions
Description:

Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.

r-base-rms 1.0
Propagated dependencies: r-survival@3.8-3 r-rms@8.1-0 r-do@2.0.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=base.rms
Licenses: GPL 3
Build system: r
Synopsis: Convert Regression Between Base Function and 'rms' Package
Description:

We perform linear, logistic, and cox regression using the base functions lm(), glm(), and coxph() in the R software and the survival package. Likewise, we can use ols(), lrm() and cph() from the rms package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. base.rms package can help you to switch between the two sets of commands easily.

r-bat 2.11.0
Propagated dependencies: r-vegan@2.7-2 r-treetools@2.0.0 r-terra@1.8-86 r-phytools@2.5-2 r-nls2@0.3-4 r-mass@7.3-65 r-hypervolume@3.1.6 r-geometry@0.5.2 r-boot@1.3-32 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=BAT
Licenses: GPL 3
Build system: r
Synopsis: Biodiversity Assessment Tools
Description:

Includes algorithms to assess alpha and beta diversity in all their dimensions (taxonomic, phylogenetic and functional). It allows performing a number of analyses based on species identities/abundances, phylogenetic/functional distances, trees, convex-hulls or kernel density n-dimensional hypervolumes depicting species relationships. Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.

r-bgev 0.2
Propagated dependencies: r-mass@7.3-65 r-envstats@3.1.0 r-deoptim@2.2-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bgev
Licenses: GPL 3
Build system: r
Synopsis: Bimodal GEV Distribution with Location Parameter
Description:

Density, distribution function, quantile function random generation and estimation of bimodal GEV distribution given in Otiniano et al. (2023) <doi:10.1007/s10651-023-00566-7>. This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas.

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-bootcluster 0.4.3
Propagated dependencies: r-sna@2.8 r-progress@1.2.3 r-network@1.19.0 r-mclust@6.1.2 r-kernlab@0.9-33 r-intergraph@2.0-4 r-igraph@2.2.1 r-gridextra@2.3 r-ggplot2@4.0.1 r-ggally@2.4.0 r-fpc@2.2-13 r-foreach@1.5.2 r-flexclust@1.5.0 r-dplyr@1.1.4 r-doparallel@1.0.17 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bootcluster
Licenses: GPL 2
Build system: r
Synopsis: Bootstrapping Estimates of Clustering Stability
Description:

Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>. Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) <doi:10.1002/sam.11593>. Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method.

r-binsegrcpp 2025.5.13
Propagated dependencies: r-rcpp@1.1.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/tdhock/binsegRcpp
Licenses: GPL 3
Build system: r
Synopsis: Efficient Implementation of Binary Segmentation
Description:

Standard template library containers are used to implement an efficient binary segmentation algorithm, which is log-linear on average and quadratic in the worst case.

r-box-lsp 0.1.3
Propagated dependencies: r-rlang@1.1.6 r-fs@1.6.6 r-cli@3.6.5 r-box@1.2.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Appsilon/box.lsp
Licenses: LGPL 3
Build system: r
Synopsis: Provides 'box' Compatibility for 'languageserver'
Description:

This package provides a box compatible custom language parser for the languageserver package to provide completion and signature hints in code editors.

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-bayesess 0.1.19
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mcmcpack@1.7-1 r-matrixmodels@0.5-4 r-mass@7.3-65 r-laplacesdemon@16.1.6 r-dfcrm@0.2-2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesESS
Licenses: GPL 2+
Build system: r
Synopsis: Determining Effective Sample Size
Description:

Determines effective sample size of a parametric prior distribution in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.

r-baseballr 1.6.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rvest@1.0.5 r-rlang@1.1.6 r-rcppparallel@5.1.11-1 r-rcpp@1.1.0 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-jsonlite@2.0.0 r-janitor@2.2.1 r-httr@1.4.7 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://billpetti.github.io/baseballr/
Licenses: Expat
Build system: r
Synopsis: Acquiring and Analyzing Baseball Data
Description:

This package provides numerous utilities for acquiring and analyzing baseball data from online sources such as Baseball Reference <https://www.baseball-reference.com/>, FanGraphs <https://www.fangraphs.com/>, and the MLB Stats API <https://www.mlb.com/>.

r-bdsvd 0.2.1
Propagated dependencies: r-irlba@2.3.5.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bdsvd
Licenses: GPL 2+
Build system: r
Synopsis: Block Structure Detection Using Singular Vectors
Description:

This package performs block diagonal covariance matrix detection using singular vectors (BD-SVD), which can be extended to hierarchical variable clustering (HC-SVD). The methods are described in Bauer (2024) <doi:10.1080/10618600.2024.2422985> and Bauer (202X) <doi:10.48550/arXiv.2308.06820>.

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Total results: 68655