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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-breadr 1.0.3
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-readr@2.1.6 r-purrr@1.2.0 r-matrixstats@1.5.0 r-mass@7.3-65 r-magrittr@2.0.4 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jonotuke/BREADR
Licenses: Expat
Build system: r
Synopsis: Estimates Degrees of Relatedness (Up to the Second Degree) for Extreme Low-Coverage Data
Description:

The goal of the package is to provide an easy-to-use method for estimating degrees of relatedness (up to the second degree) for extreme low-coverage data. The package also allows users to quantify and visualise the level of confidence in the estimated degrees of relatedness.

r-bayesfmri 0.11.0
Propagated dependencies: r-viridislite@0.4.2 r-sp@2.2-0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrixstats@1.5.0 r-matrix@1.7-4 r-mass@7.3-65 r-foreach@1.5.2 r-fmritools@0.7.2 r-excursions@2.5.11 r-ciftitools@0.18.0 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/mandymejia/BayesfMRI
Licenses: GPL 3
Build system: r
Synopsis: Spatial Bayesian Methods for Task Functional MRI Studies
Description:

This package performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the CIFTI neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.

r-bsvars 3.2
Propagated dependencies: r-stochvol@3.2.9 r-rcpptn@0.2-2 r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r6@2.6.1 r-gigrvg@0.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://bsvars.org/bsvars/
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Estimation of Structural Vector Autoregressive Models
Description:

This package provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The bsvars package is aligned regarding objects, workflows, and code structure with the R package bsvarSIGNs by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.

r-bpgmm 1.1.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pgmm@1.2.8 r-mvtnorm@1.3-3 r-mcmcse@1.5-1 r-mclust@6.1.2 r-mass@7.3-65 r-label-switching@1.8 r-gtools@3.9.5 r-fabmix@5.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bpgmm
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models
Description:

Model-based clustering using Bayesian parsimonious Gaussian mixture models. MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.

r-bayespower 1.0.2
Propagated dependencies: r-tidyr@1.3.1 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_v2>.

r-bigqf 1.6
Propagated dependencies: r-svd@0.5.8 r-matrix@1.7-4 r-coxme@2.2-22 r-compquadform@1.4.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/tslumley/bigQF
Licenses: GPL 2
Build system: r
Synopsis: Quadratic Forms in Large Matrices
Description:

This package provides a computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

r-bspbss 1.0.6
Propagated dependencies: r-svd@0.5.8 r-rstiefel@1.0.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-oro-nifti@0.11.4 r-neurobase@1.34.0 r-movmf@0.2-10 r-ica@1.0-3 r-gtools@3.9.5 r-gridextra@2.3 r-gplots@3.2.0 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-bayesgpfit@1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BSPBSS
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Spatial Blind Source Separation
Description:

Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433.

r-bayesboot 0.2.3
Propagated dependencies: r-plyr@1.8.9 r-hdinterval@0.2.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/rasmusab/bayesboot
Licenses: Expat
Build system: r
Synopsis: An Implementation of Rubin's (1981) Bayesian Bootstrap
Description:

This package provides functions for performing the Bayesian bootstrap as introduced by Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result. The implementation can handle both summary statistics that works on a weighted version of the data and summary statistics that works on a resampled data set.

r-batchexperiments 1.4.4
Propagated dependencies: r-rsqlite@2.4.4 r-dbi@1.2.3 r-data-table@1.17.8 r-checkmate@2.3.3 r-bbmisc@1.13 r-batchjobs@1.10 r-backports@1.5.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/tudo-r/BatchExperiments
Licenses: Modified BSD
Build system: r
Synopsis: Statistical Experiments on Batch Computing Clusters
Description:

Extends the BatchJobs package to run statistical experiments on batch computing clusters. For further details see the project web page.

r-binpackr 0.2.0
Propagated dependencies: r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/lschneiderbauer/binpackr
Licenses: GPL 3+
Build system: r
Synopsis: Fast 1d Bin Packing
Description:

This package implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details.

r-basecamb 1.1.5
Propagated dependencies: r-survival@3.8-3 r-sae@1.3 r-purrr@1.2.0 r-mice@3.18.0 r-mass@7.3-65 r-hmisc@5.2-4 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=basecamb
Licenses: GPL 3+
Build system: r
Synopsis: Utilities for Streamlined Data Import, Imputation and Modelling
Description:

This package provides functions streamlining the data analysis workflow: Outsourcing data import, renaming and type casting to a *.csv. Manipulating imputed datasets and fitting models on them. Summarizing models.

r-bmiselect 1.0.3
Propagated dependencies: r-stringr@1.6.0 r-rfast@2.1.5.2 r-posterior@1.6.1 r-mvnfast@0.2.8 r-mice@3.18.0 r-mcmcpack@1.7-1 r-mass@7.3-65 r-gigrvg@0.8 r-foreach@1.5.2 r-doparallel@1.0.17 r-arm@1.14-4 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BMIselect
Licenses: FSDG-compatible
Build system: r
Synopsis: Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Datasets
Description:

This package provides a suite of Bayesian MI-LASSO for variable selection methods for multiply-imputed datasets. The package includes four Bayesian MI-LASSO models using shrinkage (Multi-Laplace, Horseshoe, ARD) and Spike-and-Slab (Spike-and-Laplace) priors, along with tools for model fitting via MCMC, four-step projection predictive variable selection, and hyperparameter calibration. Methods are suitable for both continuous and binary covariates under missing-at-random or missing-completely-at-random assumptions. See Zou, J., Wang, S. and Chen, Q. (2025), Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data. ArXiv, 2211.00114. <doi:10.48550/arXiv.2211.00114> for more details. We also provide the frequentist`s MI-LASSO function.

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-biovizseq 1.0.5
Propagated dependencies: r-treeio@1.34.0 r-tidyr@1.3.1 r-stringr@1.6.0 r-shiny@1.11.1 r-seqinr@4.2-36 r-rcolorbrewer@1.1-3 r-magrittr@2.0.4 r-httr@1.4.7 r-ggtree@4.0.1 r-ggplot2@4.0.1 r-ggh4x@0.3.1 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=BioVizSeq
Licenses: Artistic License 2.0
Build system: r
Synopsis: Visualizing the Elements Within Bio-Sequences
Description:

Visualizing the types and distribution of elements within bio-sequences. At the same time, We have developed a geom layer, geom_rrect(), that can generate rounded rectangles. No external references are used in the development of this package.

r-barry 0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/USCbiostats/barryr
Licenses: Expat
Build system: r
Synopsis: Your Go-to Motif Accountant
Description:

This package provides the C++ header-only library barry for use in R packages. barry is a C++ template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. It provides tools for sparse arrays, user-defined count statistics, support set constraints, power set generation, and includes modules for Discrete Exponential Family Models (DEFMs) and network statistics. By placing these headers in this package, we offer an efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. This package follows the same approach as the BH package which provides Boost headers for R packages.

r-blpestimator 0.3.4
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-randtoolbox@2.0.5 r-numderiv@2016.8-1.1 r-mvquad@1.0-8 r-matrix@1.7-4 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BLPestimatoR
Licenses: GPL 3
Build system: r
Synopsis: Performs a BLP Demand Estimation
Description:

This package provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.

r-braggr 0.1.1
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=braggR
Licenses: GPL 2
Build system: r
Synopsis: Calculate the Revealed Aggregator of Probability Predictions
Description:

Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian aggregator that is regularized by analyzing the forecasters disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters probability predictions (p) of a future binary event and b) the forecasters common prior (p0) of the future event. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example.

r-baffle 0.2.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://j-moravec.github.io/baffle/
Licenses: Expat
Build system: r
Synopsis: Make Waffle Plots with Base Graphics
Description:

Waffle plots are rectangular pie charts that represent a quantity or abundances using colored squares or other symbol. This makes them better at transmitting information as the discrete number of squares is easier to read than the circular area of pie charts. While the original waffle charts were rectangular with 10 rows and columns, with a single square representing 1%, they are nowadays popular in various infographics to visualize any proportional ratios.

r-bestree 0.5.2
Propagated dependencies: r-plyr@1.8.9
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BESTree
Licenses: Expat
Build system: r
Synopsis: Branch-Exclusive Splits Trees
Description:

Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process. Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>.

r-bigdatape 0.0.96
Propagated dependencies: r-tibble@3.3.0 r-httr2@1.2.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: <https://github.com/StrategicProjects/bigdatape>
Licenses: Expat
Build system: r
Synopsis: Secure and Intuitive Access to 'BigDataPE' 'API' Datasets
Description:

Designed to simplify the process of retrieving datasets from the Big Data PE platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data using the httr2 package.

r-bnstruct 1.0.15
Propagated dependencies: r-igraph@2.2.1 r-bitops@1.0-9
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bnstruct
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Bayesian Network Structure Learning from Data with Missing Values
Description:

Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.

r-bprinstrattte 0.0.7
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 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-purrr@1.2.0 r-magrittr@2.0.4 r-furrr@0.3.1 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/Boehringer-Ingelheim/BPrinStratTTE
Licenses: GPL 3+
Build system: r
Synopsis: Causal Effects in Principal Strata Defined by Antidrug Antibodies
Description:

Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.

r-buildsys 1.1.2
Propagated dependencies: r-digest@0.6.39
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/pjumppanen/BuildSys
Licenses: GPL 2
Build system: r
Synopsis: System for Building and Debugging C/C++ Dynamic Libraries
Description:

This package provides a build system based on GNU make that creates and maintains (simply) make files in an R session and provides GUI debugging support through Microsoft Visual Code'.

r-bmscstan 1.2.1.0
Propagated dependencies: r-rstan@2.32.7 r-loo@2.8.0 r-logspline@2.1.22 r-laplacesdemon@16.1.6 r-ggplot2@4.0.1 r-bayesplot@1.14.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/michelescandola/bmscstan
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Multilevel Single Case Models using 'Stan'
Description:

Analyse single case analyses against a control group. Its purpose is to provide a flexible, with good power and low first type error approach that can manage at the same time controls and patient's data. The use of Bayesian statistics allows to test both the alternative and null hypothesis. Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq> Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.

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