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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-bootkmeans 1.0.0
Propagated dependencies: r-thresher@1.1.5 r-mvtnorm@1.3-3 r-mass@7.3-65 r-lmtest@0.9-40 r-fclust@2.1.3 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=bootkmeans
Licenses: GPL 2
Build system: r
Synopsis: Bootstrap Augmented k-Means Algorithm for Fuzzy Partitions
Description:

Implementation of the bootkmeans algorithm, a bootstrap augmented k-means algorithm that returns probabilistic cluster assignments. From paper by Ghashti, J.S., Andrews, J.L. Thompson, J.R.J., Epp, J. and H.S. Kochar (2025), "A bootstrap augmented k-means algorithm for fuzzy partitions" (Submitted).

r-bayescureratemodel 1.6
Propagated dependencies: r-vgam@1.1-13 r-survival@3.8-3 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mclust@6.1.2 r-hdinterval@0.2.4 r-foreach@1.5.2 r-flexsurv@2.3.2 r-doparallel@1.0.17 r-coda@0.19-4.1 r-calculus@1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/mqbssppe/Bayesian_cure_rate_model
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Cure Rate Modeling for Time-to-Event Data
Description:

This package provides a fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) <doi:10.1007/s11749-024-00942-w> and Papastamoulis and Milienos (2024b) <doi:10.48550/arXiv.2409.10221>. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.

r-belg 1.5.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://r-spatialecology.github.io/belg/
Licenses: Expat
Build system: r
Synopsis: Boltzmann Entropy of a Landscape Gradient
Description:

Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.

r-bonn 1.0.3
Propagated dependencies: r-jsonlite@2.0.0 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/sumtxt/bonn/
Licenses: GPL 3
Build system: r
Synopsis: Access INKAR Database
Description:

Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.

r-bracketeer 0.1.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/bbtheo/bracketeer
Licenses: Expat
Build system: r
Synopsis: Tournament Generator
Description:

Create and manage tournament brackets for various competition formats including single elimination, double elimination, round robin, Swiss system, and group-stage-to-knockout tournaments. Provides tools for seeding, scheduling, recording results, and tracking standings.

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-bigdatastatmeth 1.0.3
Dependencies: zlib@1.3.1
Propagated dependencies: r-rhdf5lib@1.32.0 r-rhdf5@2.54.0 r-rcurl@1.98-1.17 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-data-table@1.17.8 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=BigDataStatMeth
Licenses: Expat
Build system: r
Synopsis: Tools and Infrastructure for Developing 'Scalable' 'HDF5'-Based Methods
Description:

This package provides a framework for scalable statistical computing on large on-disk matrices stored in HDF5 files. It provides efficient block-wise implementations of core linear-algebra operations (matrix multiplication, SVD, PCA, QR decomposition, and canonical correlation analysis) written in C++ and R. These building blocks are designed not only for direct use, but also as foundational components for developing new statistical methods that must operate on datasets too large to fit in memory. The package supports data provided either as HDF5 files or standard R objects, and is intended for high-dimensional applications such as omics and precision-medicine research.

r-bifactorindicescalculator 0.2.2
Propagated dependencies: r-tidyr@1.3.1 r-mplusautomation@1.2 r-mnormt@2.1.1 r-mirt@1.45.1 r-lavaan@0.6-20
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/ddueber/BifactorIndicesCalculator
Licenses: GPL 3+
Build system: r
Synopsis: Bifactor Indices Calculator
Description:

The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the Excel based Bifactor Indices Calculator (Dueber, 2017) <doi:10.13023/edp.tool.01> with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models.

r-binnonnor 1.5.3
Propagated dependencies: r-mvtnorm@1.3-3 r-matrix@1.7-4 r-corpcor@1.6.10 r-bb@2019.10-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BinNonNor
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Data Generation with Binary and Continuous Non-Normal Components
Description:

Generation of multiple binary and continuous non-normal variables simultaneously given the marginal characteristics and association structure based on the methodology proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

r-blosc 0.1.2
Dependencies: zlib@1.3.1
Propagated dependencies: r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://pepijn-devries.github.io/blosc/
Licenses: GPL 3+
Build system: r
Synopsis: Compress and Decompress Data Using the 'BLOSC' Library
Description:

Arrays of structured data types can require large volumes of disk space to store. Blosc is a library that provides a fast and efficient way to compress such data. It is often applied in storage of n-dimensional arrays, such as in the case of the geo-spatial zarr file format. This package can be used to compress and decompress data using Blosc'.

r-balancecheck 0.2
Propagated dependencies: r-mvtnorm@1.3-3 r-ade4@1.7-23
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BalanceCheck
Licenses: GPL 2+
Build system: r
Synopsis: Balance Check for Multiple Covariates in Matched Observational Studies
Description:

Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.

r-buddle 2.0.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 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=Buddle
Licenses: GPL 2
Build system: r
Synopsis: Deep Learning for Statistical Classification and Regression Analysis with Random Effects
Description:

Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) <DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber (2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al. (1998) <DOI:10.1109/5.726791> for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data.

r-bespatial 0.1.3
Propagated dependencies: r-tibble@3.3.0 r-terra@1.8-86 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-landscapemetrics@2.2.1 r-comat@0.9.7 r-belg@1.5.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://jakubnowosad.com/bespatial/
Licenses: Expat
Build system: r
Synopsis: Boltzmann Entropy for Spatial Data
Description:

Calculates several entropy metrics for spatial data inspired by Boltzmann's entropy formula. It includes metrics introduced by Cushman for landscape mosaics (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), and landscape gradients and point patterns (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>); and by Gao et al. for landscape gradients (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).

r-bidistances 0.1.3
Dependencies: pandoc@2.19.2
Propagated dependencies: r-vegan@2.7-2 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pracma@2.4.6 r-paralleldist@0.2.7 r-ggplot2@4.0.1 r-e1071@1.7-16 r-diptest@0.77-2 r-datavisualizations@1.4.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BIDistances
Licenses: GPL 3
Build system: r
Synopsis: Bioinformatic Distances
Description:

This package provides a selection of distances measures for bioinformatics data. Other important distance measures for bioinformatics data are selected from the R package parallelDist'. A special distance measure for the Gene Ontology is available.

r-blockr-ggplot 0.1.0
Propagated dependencies: r-shinywidgets@0.9.0 r-shinyjs@2.1.0 r-shiny@1.11.1 r-patchwork@1.3.2 r-glue@1.8.0 r-ggplot2@4.0.1 r-colourpicker@1.3.0 r-blockr-core@0.1.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://bristolmyerssquibb.github.io/blockr.ggplot/
Licenses: GPL 3+
Build system: r
Synopsis: Interactive 'ggplot2' Visualization Blocks
Description:

Extends blockr.core with interactive blocks for data visualization using ggplot2'. Users can build charts through a graphical interface without writing code directly. Includes common chart types (bar charts, line charts, pie charts, scatter plots) as well as statistical plots (boxplots, histograms, density plots, violin plots) with rich customization options and intuitive user interfaces.

r-behavr 0.3.3
Propagated dependencies: r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/rethomics/behavr
Licenses: GPL 3
Build system: r
Synopsis: Canonical Data Structure for Behavioural Data
Description:

This package implements an S3 class based on data.table to store and process efficiently ethomics (high-throughput behavioural) data.

r-brainkcca 0.1.0
Propagated dependencies: r-rgl@1.3.31 r-oro-nifti@0.11.4 r-misc3d@0.9-1 r-knitr@1.50 r-kernlab@0.9-33 r-elasticnet@1.3 r-cca@1.2.2 r-brainr@1.7.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=brainKCCA
Licenses: LGPL 2.0+
Build system: r
Synopsis: Region-Level Connectivity Network Construction via Kernel Canonical Correlation Analysis
Description:

It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.

r-binseqtest 1.0.4
Propagated dependencies: r-clinfun@1.1.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=binseqtest
Licenses: GPL 3
Build system: r
Synopsis: Exact Binary Sequential Designs and Analysis
Description:

For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.

r-braidreports 1.0.5
Propagated dependencies: r-scales@1.4.0 r-gtable@0.3.6 r-ggplot2@4.0.1 r-cowplot@1.2.0 r-braidrm@1.0.6 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=braidReports
Licenses: GPL 3+
Build system: r
Synopsis: Visualize Combined Action Response Surfaces and Report BRAID Analyses
Description:

This package provides functions to visualize combined action data in ggplot2'. Also provides functions for producing full BRAID analysis reports with custom layouts and aesthetics, using the BRAID method originally described in Twarog et al. (2016) <doi:10.1038/srep25523>.

r-birdring 1.6
Propagated dependencies: r-raster@3.6-32 r-lazydata@1.1.0 r-ks@1.15.1 r-geosphere@1.5-20
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=birdring
Licenses: GPL 2
Build system: r
Synopsis: Methods to Analyse Ring Re-Encounter Data
Description:

R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to <doi:10.1080/03078698.2014.933053>.

r-breathteststan 0.8.9
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-purrr@1.2.0 r-dplyr@1.1.4 r-breathtestcore@0.8.10 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/dmenne/breathteststan
Licenses: GPL 3+
Build system: r
Synopsis: Stan-Based Fit to Gastric Emptying Curves
Description:

Stan-based curve-fitting function for use with package breathtestcore by the same author. Stan functions are refactored here for easier testing.

r-bayesgp 0.1.3
Propagated dependencies: r-tmbstan@1.0.91 r-tmb@1.9.18 r-sfsmisc@1.1-23 r-rstan@2.32.7 r-rcppeigen@0.3.4.0.2 r-numderiv@2016.8-1.1 r-matrix@1.7-4 r-laplacesdemon@16.1.6 r-fda@6.3.0 r-aghq@0.4.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesGP
Licenses: GPL 3+
Build system: r
Synopsis: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models
Description:

This package implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.

r-bawir 1.5
Propagated dependencies: r-xml2@1.5.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-stringi@1.8.7 r-rvest@1.0.5 r-robotstxt@0.7.15 r-reshape2@1.4.5 r-purrr@1.2.0 r-polite@0.1.3 r-plyr@1.8.9 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-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://www.uv.es/vivigui/basketball_platform.html
Licenses: GPL 2+
Build system: r
Synopsis: Analysis of Basketball Data
Description:

Collection of tools to work with European basketball data. Functions available are related to friendly web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, <https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play and spatial shooting data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players percentiles, plots of players monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls, offensive rebounds and different types of shooting charts. Please see Vinue (2020) <doi:10.1089/big.2018.0124> and Vinue (2024) <doi:10.1089/big.2023.0177>.

r-bases 0.2.0
Propagated dependencies: r-rlang@1.1.6 r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://corymccartan.com/bases/
Licenses: Expat
Build system: r
Synopsis: Basis Expansions for Regression Modeling
Description:

This package provides various basis expansions for flexible regression modeling, including random Fourier features (Rahimi & Recht, 2007) <https://proceedings.neurips.cc/paper_files/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf>, exact kernel / Gaussian process feature maps, prior features for Bayesian Additive Regression Trees (BART) (Chipman et al., 2010) <doi:10.1214/09-AOAS285>, and a helpful interface for n-way interactions. The provided functions may be used within any modeling formula, allowing the use of kernel methods and other basis expansions in modeling functions that do not otherwise support them. Along with the basis expansions, a number of kernel functions are also provided, which support kernel arithmetic to form new kernels. Basic ridge regression functionality is included as well.

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