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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-burnr 0.6.1
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-plyr@1.8.9 r-mass@7.3-65 r-ggplot2@4.0.1 r-forcats@1.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/ltrr-arizona-edu/burnr/
Licenses: GPL 3+
Build system: r
Synopsis: Forest Fire History Analysis
Description:

This package provides tools to read, write, parse, and analyze forest fire history data (e.g. FHX). Described in Malevich et al. (2018) <doi:10.1016/j.dendro.2018.02.005>.

r-bdscale 2.0.0
Propagated dependencies: r-scales@1.4.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://github.com/dvmlls/bdscale
Licenses: GPL 2
Build system: r
Synopsis: Remove Weekends and Holidays from ggplot2 Axes
Description:

This package provides a continuous date scale, omitting weekends and holidays.

r-bgumbel 0.0.3
Dependencies: gfortran@14.3.0 gcc@14.3.0
Propagated dependencies: r-sparsem@1.84-2 r-quantreg@6.1 r-mcmcpack@1.7-1 r-mass@7.3-65 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=bgumbel
Licenses: Expat
Build system: r
Synopsis: Bimodal Gumbel Distribution
Description:

Bimodal Gumbel distribution. General functions for performing extreme value analysis.

r-batchgetsymbols 2.6.4
Propagated dependencies: r-zoo@1.8-14 r-xml@3.99-0.20 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-scales@1.4.0 r-rvest@1.0.5 r-quantmod@0.4.28 r-purrr@1.2.0 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-future@1.68.0 r-furrr@0.3.1 r-dplyr@1.1.4 r-curl@7.0.0 r-crayon@1.5.3 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=BatchGetSymbols
Licenses: GPL 2
Build system: r
Synopsis: Downloads and Organizes Financial Data for Multiple Tickers
Description:

Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.

r-bracod-r 0.0.2.0
Propagated dependencies: r-reticulate@1.44.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BRACoD.R
Licenses: Expat
Build system: r
Synopsis: BRACoD: Bayesian Regression Analysis of Compositional Data
Description:

The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.

r-bbest 0.1-8
Propagated dependencies: r-shiny@1.11.1 r-reshape2@1.4.5 r-ggplot2@4.0.1 r-deoptim@2.2-8 r-aws@2.5-6
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BBEST
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Estimation of Incoherent Neutron Scattering Backgrounds
Description:

We implemented a Bayesian-statistics approach for subtraction of incoherent scattering from neutron total-scattering data. In this approach, the estimated background signal associated with incoherent scattering maximizes the posterior probability, which combines the likelihood of this signal in reciprocal and real spaces with the prior that favors smooth lines. The description of the corresponding approach could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.

r-bdl 1.0.5
Propagated dependencies: r-tmaptools@3.3 r-tmap@4.2 r-tidyr@1.3.1 r-tibble@3.3.0 r-sf@1.0-23 r-randomcolor@1.1.0.1 r-purrr@1.2.0 r-progress@1.2.3 r-magrittr@2.0.4 r-jsonlite@2.0.0 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://statisticspoland.github.io/R_Package_to_API_BDL/
Licenses: GPL 3
Build system: r
Synopsis: Interface and Tools for 'BDL' API
Description:

Interface to Local Data Bank ('Bank Danych Lokalnych - bdl') API <https://api.stat.gov.pl/Home/BdlApi?lang=en> with set of useful tools like quick plotting and map generating using data from bank.

r-bayesmallows 2.2.7
Propagated dependencies: r-testthat@3.3.0 r-sets@1.0-25 r-rlang@1.1.6 r-relations@0.6-15 r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/ocbe-uio/BayesMallows
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Preference Learning with the Mallows Rank Model
Description:

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>; Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>; Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

r-bmixture 1.7
Propagated dependencies: r-bdgraph@2.74
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://www.uva.nl/profile/a.mohammadi
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Estimation for Finite Mixture of Distributions
Description:

This package provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.

r-bgmm 1.8.5
Propagated dependencies: r-mvtnorm@1.3-3 r-lattice@0.22-7 r-combinat@0.0-8 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://bgmm.molgen.mpg.de/
Licenses: GPL 3
Build system: r
Synopsis: Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling
Description:

Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.

r-biomass 2.2.4-1
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-rappdirs@0.3.3 r-proj4@1.0-15 r-minpack-lm@1.2-4 r-jsonlite@2.0.0 r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://umr-amap.github.io/BIOMASS/
Licenses: GPL 2
Build system: r
Synopsis: Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests
Description:

This package contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite â BIOMASSâ , please use citation(â BIOMASSâ ). For more information, see Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.

r-beast 1.2
Propagated dependencies: r-rcolorbrewer@1.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=beast
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series
Description:

Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) <doi:10.1515/ijb-2018-0052> for a detailed presentation of the method.

r-bexy 0.1.3
Propagated dependencies: r-ternary@2.3.6 r-teachingdemos@2.13
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bexy
Licenses: GPL 2
Build system: r
Synopsis: Visualize and Parse the Output of 'BeXY'
Description:

This package provides functions for summarizing and plotting the output of the command-line tool BeXY (<https://bitbucket.org/wegmannlab/bexy>), a tool that performs Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.

r-blindspiker 0.2.0
Propagated dependencies: r-tidyr@1.3.1 r-magrittr@2.0.4 r-gt@1.3.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bingroup@2.2-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/markhogue/blindspiker
Licenses: GPL 3
Build system: r
Synopsis: Laboratory Blind Spike Sample Analyses
Description:

This package provides a blind spike program provides samples to a laboratory in order to perform quality control (QC) checks. The samples provided are of a known quantity to the tester. The laboratory is typically uninformed of that the sample provided is a QC sample.

r-bestie 0.1.5
Propagated dependencies: r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-bidag@2.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=Bestie
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Estimation of Intervention Effects
Description:

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.

r-bnmonitor 0.2.2
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-qgraph@1.9.8 r-purrr@1.2.0 r-igraph@2.2.1 r-grbase@2.0.3 r-grain@1.4.6 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bnlearn@5.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://manueleleonelli.github.io/bnmonitor/
Licenses: GPL 3
Build system: r
Synopsis: An Implementation of Sensitivity Analysis in Bayesian Networks
Description:

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.

r-bcra 2.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BCRA
Licenses: GPL 2+
Build system: r
Synopsis: Breast Cancer Risk Assessment
Description:

This package provides functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.

r-bivrp 1.2-2
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=bivrp
Licenses: GPL 2+
Build system: r
Synopsis: Bivariate Residual Plots with Simulation Polygons
Description:

Generates bivariate residual plots with simulation polygons for any diagnostics and bivariate model from which functions to extract the desired diagnostics, simulate new data and refit the models are available.

r-bivariateleaflet 0.1.0
Propagated dependencies: r-sf@1.0-23 r-rlang@1.1.6 r-leaflet@2.2.3 r-htmltools@0.5.8.1 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=bivariateLeaflet
Licenses: Expat
Build system: r
Synopsis: Create Bivariate Choropleth Maps with 'Leaflet'
Description:

This package creates bivariate choropleth maps using Leaflet'. This package provides tools for visualizing the relationship between two variables through a color matrix representation on an interactive map.

r-bayesroe 0.2
Propagated dependencies: r-shinybs@0.61.1 r-shiny@1.11.1 r-scales@1.4.0 r-golem@0.5.1 r-ggplot2@4.0.1 r-config@0.3.2 r-colourpicker@1.3.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/waidschrat/bayesROE
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Regions of Evidence
Description:

Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (<https://osf.io/jxnsv>). Besides generic functions, the package also provides an intuitive Shiny application, that can be run in local R environments.

r-bayesertools 0.2.4
Propagated dependencies: r-tidyr@1.3.1 r-tidybayes@3.0.7 r-rstanemax@0.1.9 r-rstanarm@2.32.2 r-rlang@1.1.6 r-purrr@1.2.0 r-posterior@1.6.1 r-loo@2.8.0 r-gt@1.3.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://genentech.github.io/BayesERtools/
Licenses: ASL 2.0
Build system: r
Synopsis: Bayesian Exposure-Response Analysis Tools
Description:

Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at <https://genentech.github.io/BayesERbook/>.

r-barcoder 0.1.7
Propagated dependencies: r-shiny@1.11.1 r-rstudioapi@0.17.1 r-qrcode@0.3.0 r-miniui@0.1.2 r-dt@0.34.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://docs.ropensci.org/baRcodeR/https://github.com/ropensci/baRcodeR/
Licenses: GPL 3
Build system: r
Synopsis: Label Creation for Tracking and Collecting Data from Biological Samples
Description:

This package provides tools to generate unique identifier codes and printable barcoded labels for the management of biological samples. The creation of unique ID codes and printable PDF files can be initiated by standard commands, user prompts, or through a GUI addin for R Studio. Biologically informative codes can be included for hierarchically structured sampling designs.

r-biorssay 1.1.0
Propagated dependencies: r-colorspace@2.1-2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://milesilab.github.io/BioRssay/
Licenses: AGPL 3+
Build system: r
Synopsis: Analyze Bioassays and Probit Graphs
Description:

This package provides a robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations.

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.

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Total results: 69112