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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.

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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-bennu 0.3.2
Propagated dependencies: r-tidyr@1.3.1 r-tidybayes@3.0.7 r-stanheaders@2.32.10 r-scales@1.4.0 r-rstantools@2.5.0 r-rstan@2.32.7 r-rlang@1.1.6 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-generics@0.1.4 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://sempwn.github.io/bennu/
Licenses: Expat
Build system: r
Synopsis: Bayesian Estimation of Naloxone Kit Number Under-Reporting
Description:

Bayesian model and associated tools for generating estimates of total naloxone kit numbers distributed and used from naloxone kit orders data. Provides functions for generating simulated data of naloxone kit use and functions for generating samples from the posterior.

r-bregr 1.4.0
Propagated dependencies: r-vctrs@0.6.5 r-tibble@3.3.0 r-survival@3.8-3 r-s7@0.2.1 r-rlang@1.1.6 r-purrr@1.2.0 r-mirai@2.5.2 r-lifecycle@1.0.4 r-insight@1.4.3 r-glue@1.8.0 r-ggplot2@4.0.1 r-forestploter@1.1.3 r-dplyr@1.1.4 r-cli@3.6.5 r-broom-helpers@1.22.0 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/WangLabCSU/bregr
Licenses: GPL 3+
Build system: r
Synopsis: Easy and Efficient Batch Processing of Regression Models
Description:

Easily processes batches of univariate or multivariate regression models. Returns results in a tidy format and generates visualization plots for straightforward interpretation (Wang, Shixiang, et al. (2025) <DOI:10.1002/mdr2.70028>).

r-betacal 0.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=betacal
Licenses: Expat
Build system: r
Synopsis: Beta Calibration
Description:

Fit beta calibration models and obtain calibrated probabilities from them.

r-berryfunctions 1.22.13
Propagated dependencies: r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/brry/berryFunctions
Licenses: GPL 2+
Build system: r
Synopsis: Function Collection Related to Plotting and Hydrology
Description:

Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.

r-brinda 0.1.5
Propagated dependencies: r-rlang@1.1.6 r-hmisc@5.2-4 r-dplyr@1.1.4 r-data-table@1.17.8 r-berryfunctions@1.22.13
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/hanqiluo/BRINDA
Licenses: FSDG-compatible
Build system: r
Synopsis: Computation of BRINDA Adjusted Micronutrient Biomarkers for Inflammation
Description:

Inflammation can affect many micronutrient biomarkers and can thus lead to incorrect diagnosis of individuals and to over- or under-estimate the prevalence of deficiency in a population. Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) is a multi-agency and multi-country partnership designed to improve the interpretation of nutrient biomarkers in settings of inflammation and to generate context-specific estimates of risk factors for anemia (Suchdev (2016) <doi:10.3945/an.115.010215>). In the past few years, BRINDA published a series of papers to provide guidance on how to adjust micronutrient biomarkers, retinol binding protein, serum retinol, serum ferritin by Namaste (2020), soluble transferrin receptor (sTfR), serum zinc, serum and Red Blood Cell (RBC) folate, and serum B-12, using inflammation markers, alpha-1-acid glycoprotein (AGP) and/or C-Reactive Protein (CRP) by Namaste (2020) <doi:10.1093/ajcn/nqaa141>, Rohner (2017) <doi:10.3945/ajcn.116.142232>, McDonald (2020) <doi:10.1093/ajcn/nqz304>, and Young (2020) <doi:10.1093/ajcn/nqz303>. The BRINDA inflammation adjustment method mainly focuses on Women of Reproductive Age (WRA) and Preschool-age Children (PSC); however, the general principle of the BRINDA method might apply to other population groups. The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method, as described above. The BRINDA R package will first carry out rigorous checks and provides users guidance to correct data or input errors (if they occur) prior to inflammation adjustments. After no errors are detected, the package implements the BRINDA inflammation adjustment for up to five micronutrient biomarkers, namely retinol-binding-protein, serum retinol, serum ferritin, sTfR, and serum zinc (when appropriate), using inflammation indicators of AGP and/or CRP for various population groups. Of note, adjustment for serum and RBC folate and serum B-12 is not included in the R package, since evidence shows that no adjustment is needed for these micronutrient biomarkers in either WRA or PSC groups (Young (2020) <doi:10.1093/ajcn/nqz303>).

r-bibliometrixdata 0.3.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bibliometrixData
Licenses: Expat
Build system: r
Synopsis: Bibliometrix Example Datasets
Description:

It contains some example datasets used in bibliometrix'. The data are bibliographic datasets exported from the SCOPUS (<https://scopus.com>) and Clarivate Analytics Web of Science (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package bibliometrix (<https://bibliometrix.org>).

r-blrshiny2 0.1.0
Propagated dependencies: r-shiny@1.11.1 r-rmarkdown@2.30 r-rhandsontable@0.3.8 r-ggplot2@4.0.1 r-e1071@1.7-16 r-dplyr@1.1.4 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BLRShiny2
Licenses: GPL 2
Build system: r
Synopsis: Interactive Document for Working with Binary Logistic Regression Analysis
Description:

An interactive document on the topic of binary logistic regression analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/BinaryLogisticRegressionModelling/>.

r-bpacc 0.0-2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bpAcc
Licenses: GPL 2
Build system: r
Synopsis: Blood Pressure Device Accuracy Evaluation: Statistical Considerations
Description:

This package provides a comprehensive statistical analysis of the accuracy of blood pressure devices based on the method of AAMI/ANSI SP10 standards developed by the AAMI Sphygmomanometer Committee for indirect measurement of blood pressure, incorporated into IS0 81060-2. The bpAcc package gives the exact probability of accepting a device D derived from the join distribution of the sample standard deviation and a non-linear transformation of the sample mean for a specified sample size introduced by Chandel et al. (2023) and by the Association for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8).

r-brea 0.4.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=brea
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Recurrent Events Analysis
Description:

This package provides functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.

r-blsbandit 0.1
Propagated dependencies: r-zoo@1.8-14 r-shiny@1.11.1 r-rsqlite@2.4.4 r-plotly@4.11.0 r-jsonlite@2.0.0 r-dbi@1.2.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=blsBandit
Licenses: Expat
Build system: r
Synopsis: Data Viewer for Bureau of Labor Statistics Data
Description:

Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) <https://www.bls.gov>. Currently unemployment data series U1-U6 are available. Not affiliated with the Bureau of Labor Statistics or United States Government.

r-bayesforecast 1.0.5
Propagated dependencies: r-zoo@1.8-14 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-prophet@1.1.7 r-mass@7.3-65 r-lubridate@1.9.4 r-loo@2.8.0 r-gridextra@2.3 r-ggplot2@4.0.1 r-forecast@8.24.0 r-bridgesampling@1.2-1 r-bh@1.87.0-1 r-bayesplot@1.14.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bayesforecast
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Time Series Modeling with Stan
Description:

Fit Bayesian time series models using Stan for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

r-bayesics 2.0.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-survival@3.8-3 r-rlang@1.1.6 r-patchwork@1.3.2 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-janitor@2.2.1 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-future@1.68.0 r-extradistr@1.10.0 r-dplyr@1.1.4 r-dfba@0.1.0 r-cluster@2.1.8.1 r-bms@0.3.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/dksewell/bayesics
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Analyses for One- and Two-Sample Inference and Regression Methods
Description:

Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) <doi:10.1214/aoms/1177693507> or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) <doi:10.1080/01621459.1995.10476572>. ROPE bounds are based on discussions in Kruschke (2018) <doi:10.1177/2515245918771304>. Methods for determining the number of posterior samples required are described in Doss et al. (2014) <doi:10.1214/14-EJS957>. Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) <doi:10.18637/jss.v068.i04>. Methods for contingency table analysis is described in Gunel et al. (1974) <doi:10.1093/biomet/61.3.545>. Variational Bayes (VB) methods are described in Salimans and Knowles (2013) <doi:10.1214/13-BA858>. Mediation analysis uses the framework described in Imai et al. (2010) <doi:10.1037/a0020761>. The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) <doi:10.1093/biomet/asz006>. Non-parametric survival methods are described in Qing et al. (2023) <doi:10.1002/pst.2256>. Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) <doi:10.1080/03610926.2017.1388402> and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) <doi:10.1080/03610926.2018.1549247>. Correlation analysis methods are carried out by Barch and Chechile (2023) <doi:10.32614/CRAN.package.DFBA>, and described in Lindley and Phillips (1976) <doi:10.1080/00031305.1976.10479154> and Chechile and Barch (2021) <doi:10.1016/j.jmp.2021.102638>. See also Chechile (2020, ISBN: 9780262044585).

r-bootstrapfp 0.4.6
Propagated dependencies: r-sampling@2.11
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bootstrapFP
Licenses: GPL 3
Build system: r
Synopsis: Bootstrap Algorithms for Finite Population Inference
Description:

Finite Population bootstrap algorithms to estimate the variance of the Horvitz-Thompson estimator for single-stage sampling. For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) <doi:10.1214/16-SS113>.

r-bmemapping 1.2.2
Propagated dependencies: r-mvtnorm@1.3-3 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://github.com/KinsprideDuah/BMEmapping
Licenses: Expat
Build system: r
Synopsis: Spatial Interpolation using Bayesian Maximum Entropy (BME)
Description:

This package provides an accessible and robust implementation of core BME methodologies for spatial prediction. It enables the systematic integration of heterogeneous data sources including both hard data (precise measurements) and soft interval data (bounded or uncertain observations) while incorporating prior knowledge and supporting variogram-based spatial modeling. The BME methodology is described in Christakos (1990) <doi:10.1007/BF00890661> and Serre and Christakos (1999) <doi:10.1007/s004770050029>.

r-brpl 1.0.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BRPL
Licenses: Expat
Build system: r
Synopsis: Methods for Bivariate Poverty Line Calculations
Description:

This package provides tools for identifying subgroups within populations based on individual response patterns to specific interventions or treatments. Designed to support researchers and clinicians in exploring heterogeneous treatment effects and developing personalized therapeutic strategies. Offers functionality for analyzing and visualizing the interplay between two variables, thereby enhancing the interpretation of social sustainability metrics. The package focuses on bivariate discriminant analysis and aims to clarify relationships between indicator variables.

r-beastier 2.5.2
Propagated dependencies: r-xml2@1.5.0 r-tibble@3.3.0 r-stringr@1.6.0 r-sessioninfo@1.2.3 r-rlang@1.1.6 r-rjava@1.0-11 r-readr@2.1.6 r-rappdirs@0.3.3 r-phangorn@2.12.1 r-beautier@2.6.12 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://docs.ropensci.org/beastier/https://github.com/ropensci/beastier/
Licenses: GPL 3
Build system: r
Synopsis: Call 'BEAST2'
Description:

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is a command-line tool. This package provides a way to call BEAST2 from an R function call.

r-bartmachine 1.4.1.1
Dependencies: openjdk@25
Propagated dependencies: r-rjava@1.0-11 r-randomforest@4.7-1.2 r-missforest@1.6.1 r-matrixstats@1.5.0 r-ggplot2@4.0.1 r-checkmate@2.3.3 r-bartmachinejars@1.2.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bartMachine
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Additive Regression Trees
Description:

An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

r-bayesimages 0.7-0
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://bitbucket.org/Azeari/bayesimages
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Bayesian Methods for Image Segmentation using a Potts Model
Description:

Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to Moores, Pettitt & Mengersen (2020) <doi:10.1007/978-3-030-42553-1_6> for an overview and also to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details of specific algorithms.

r-bayessurvival 0.2.0
Propagated dependencies: r-survival@3.8-3 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=BayesSurvival
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Survival Analysis for Right Censored Data
Description:

This package performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure. A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two. The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" <arXiv:2005.02889>. In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification.

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-bittermelon 2.2.1
Propagated dependencies: r-unicode@16.0.0-1 r-png@0.1-8 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://trevorldavis.com/R/bittermelon/
Licenses: Expat
Build system: r
Synopsis: Bitmap Tools
Description:

This package provides functions for creating, modifying, and displaying bitmaps including printing them in the terminal. There is a special emphasis on monochrome bitmap fonts and their glyphs as well as colored pixel art/sprites. Provides native read/write support for the hex and yaff bitmap font formats and if monobit <https://github.com/robhagemans/monobit> is installed can also read/write several additional bitmap font formats.

r-bs4dash 2.3.5
Propagated dependencies: r-waiter@0.2.5-1.927501b r-shiny@1.11.1 r-rlang@1.1.6 r-lifecycle@1.0.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-httpuv@1.6.16 r-htmltools@0.5.8.1 r-fresh@0.2.2 r-cli@3.6.5 r-bslib@0.9.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/RinteRface/bs4Dash
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: 'Bootstrap 4' Version of 'shinydashboard'
Description:

Make Bootstrap 4 Shiny dashboards. Use the full power of AdminLTE3', a dashboard template built on top of Bootstrap 4 <https://github.com/ColorlibHQ/AdminLTE>.

r-basifor 0.4.1
Propagated dependencies: r-rodbc@1.3-26.1 r-measurements@1.5.1 r-httr@1.4.7 r-hmisc@5.2-4 r-foreign@0.8-90 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=basifoR
Licenses: GPL 3
Build system: r
Synopsis: Retrieval and Processing of the Spanish National Forest Inventory
Description:

Data sets of the Spanish National Forest Inventory <https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible.html> are processed to compute tree metrics and statistics. Function metrics2Vol() controls most of the routines.

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).

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Total results: 68658