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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-benford-analysis 0.1.5
Propagated dependencies: r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://github.com/carloscinelli/benford.analysis
Licenses: GPL 3
Build system: r
Synopsis: Benford Analysis for Data Validation and Forensic Analytics
Description:

This package provides tools that make it easier to validate data using Benford's Law.

r-bend 1.1
Propagated dependencies: r-rjags@4-17 r-label-switching@1.8 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/crohlo/BEND
Licenses: Expat
Build system: r
Synopsis: Bayesian Estimation of Nonlinear Data (BEND)
Description:

This package provides a set of models to estimate nonlinear longitudinal data using Bayesian estimation methods. These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) <https://hdl.handle.net/11299/252533> and Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) <doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) <doi:10.1037/met0000358>).

r-bayesqrsurvey 0.1.4
Dependencies: lapack@3.12.1
Propagated dependencies: r-rlang@1.1.6 r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pracma@2.4.6 r-posterior@1.6.1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/torodriguezt/bayesQRsurvey
Licenses: Expat
Build system: r
Synopsis: Bayesian Quantile Regression Models for Complex Survey Data Analysis
Description:

This package provides Bayesian quantile regression models for complex survey data under informative sampling using survey-weighted estimators. Both single- and multiple-output models are supported. To accelerate computation, all algorithms are implemented in C++ using Rcpp', RcppArmadillo', and RcppEigen', and are called from R'. See Nascimento and Gonçalves (2024) <doi:10.1093/jssam/smae015> and Nascimento and Gonçalves (2025, in press) <https://academic.oup.com/jssam>.

r-bcrp 1.0.2
Propagated dependencies: r-yyjsonr@0.1.21 r-tibble@3.3.0 r-readr@2.1.6 r-httr2@1.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/JulioCollazos64/bcRP
Licenses: GPL 3+
Build system: r
Synopsis: Access 'BCRPDATA' API
Description:

Search and access more than ten thousand datasets included in BCRPDATA (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).

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-bysykkel 0.3.1
Propagated dependencies: r-tibble@3.3.0 r-lubridate@1.9.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-glue@1.8.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://github.com/imangR/bysykkel
Licenses: Expat
Build system: r
Synopsis: Get City Bike Data from Norway
Description:

This package provides functions to get and download city bike data from the website and API service of each city bike service in Norway. The package aims to reduce time spent on getting Norwegian city bike data, and lower barriers to start analyzing it. The data is retrieved from Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.

r-btsr 1.0.2
Propagated dependencies: r-rdpack@2.6.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BTSR
Licenses: GPL 3+
Build system: r
Synopsis: Bounded Time Series Regression
Description:

Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in FORTRAN, which translates into very fast algorithms.

r-bssm 2.0.3
Dependencies: pandoc@2.19.2
Propagated dependencies: r-tidyr@1.3.1 r-sitmo@2.0.2 r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ramcmc@0.1.2 r-posterior@1.6.1 r-dplyr@1.1.4 r-diagis@0.2.3 r-coda@0.19-4.1 r-checkmate@2.3.3 r-bayesplot@1.14.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/helske/bssm
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models
Description:

Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

r-bayesianvars 0.1.8
Propagated dependencies: r-stochvol@3.2.9 r-scales@1.4.0 r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-mass@7.3-65 r-lpsolveapi@5.5.2.0-17.14 r-gigrvg@0.8 r-factorstochvol@1.1.1 r-colorspace@2.1-2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/luisgruber/bayesianVARs
Licenses: GPL 3+
Build system: r
Synopsis: MCMC Estimation of Bayesian Vectorautoregressions
Description:

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2025) <doi:10.1016/j.ijforecast.2025.02.001>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.

r-btergm 1.11.1
Propagated dependencies: r-statnet-common@4.12.0 r-sna@2.8 r-rocr@1.0-11 r-network@1.19.0 r-matrix@1.7-4 r-igraph@2.2.1 r-ergm@4.12.0 r-coda@0.19-4.1 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/leifeld/btergm
Licenses: GPL 2+
Build system: r
Synopsis: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
Description:

Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

r-bayesianmediationa 1.0.1
Propagated dependencies: r-survival@3.8-3 r-r2jags@0.8-9 r-lattice@0.22-7 r-gplots@3.2.0 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesianMediationA
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Mediation Analysis
Description:

We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.

r-baf 0.0.4
Propagated dependencies: r-readr@2.1.6 r-glue@1.8.0 r-fs@1.6.6 r-dplyr@1.1.4 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://christophertkenny.com/baf/
Licenses: Expat
Build system: r
Synopsis: Block Assignment Files
Description:

Download and read US Census Bureau data relationship files. Provides support for cleaning and using block assignment files since 2010, as described in <https://www.census.gov/geographies/reference-files/time-series/geo/block-assignment-files.html>. Also includes support for working with block equivalency files, used for years outside of decennial census years.

r-blockr-core 0.1.1
Propagated dependencies: r-vctrs@0.6.5 r-shinyfiles@0.9.3 r-shiny@1.11.1 r-rlang@1.1.6 r-jsonlite@2.0.0 r-htmltools@0.5.8.1 r-glue@1.8.0 r-generics@0.1.4 r-evaluate@1.0.5 r-dt@0.34.0 r-digest@0.6.39 r-cli@3.6.5 r-bslib@0.9.0 r-bsicons@0.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://bristolmyerssquibb.github.io/blockr.core/
Licenses: GPL 3+
Build system: r
Synopsis: Graphical Web-Framework for Data Manipulation and Visualization
Description:

This package provides a framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks.

r-bigdawg 3.0.3
Propagated dependencies: r-xml@3.99-0.20 r-httr@1.4.7 r-haplo-stats@1.9.7
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://tools.immunogenomics.org/
Licenses: GPL 3+
Build system: r
Synopsis: Case-Control Analysis of Multi-Allelic Loci
Description:

Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.

r-brazildataapi 0.2.0
Propagated dependencies: r-tibble@3.3.0 r-scales@1.4.0 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/lightbluetitan/brazildataapi
Licenses: GPL 3
Build system: r
Synopsis: Access Brazilian Data via APIs and Curated Datasets
Description:

This package provides functions to access data from the BrasilAPI', REST Countries API', Nager.Date API', and World Bank API', related to Brazil's postal codes, banks, holidays, company registrations, international country indicators, public holidays information, and economic development data. Additionally, the package includes curated datasets related to Brazil, covering topics such as demographic data (males and females by state and year), river levels, environmental emission factors, film festivals, and yellow fever outbreak records. The package supports research and analysis focused on Brazil by integrating open APIs with high-quality datasets from multiple domains. For more information on the APIs, see: BrasilAPI <https://brasilapi.com.br/>, Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and REST Countries API <https://restcountries.com/>.

r-bootnet 1.7.1
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-snow@0.4-4 r-rlang@1.1.6 r-qgraph@1.9.8 r-pbapply@1.7-4 r-networktools@1.6.0 r-networktoolbox@1.4.4 r-mvtnorm@1.3-3 r-mgm@1.2-15 r-matrix@1.7-4 r-isingsampler@0.2.4 r-isingfit@0.4 r-igraph@2.2.1 r-gtools@3.9.5 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-corpcor@1.6.10 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/SachaEpskamp/bootnet
Licenses: GPL 2
Build system: r
Synopsis: Bootstrap Methods for Various Network Estimation Routines
Description:

Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.

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-bttl 1.0.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BTTL
Licenses: GPL 3
Build system: r
Synopsis: Bradley-Terry Transfer Learning
Description:

This package implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2024) <doi:10.48550/arXiv.2408.10558>, and allows for the statistical modeling of multi-attribute pairwise comparison data.

r-bartxviz 1.0.11
Propagated dependencies: r-tidyr@1.3.1 r-superlearner@2.0-29 r-stringr@1.6.0 r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-missforest@1.6.1 r-gridextra@2.3 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-gggenes@0.5.1 r-ggforce@0.5.0 r-ggfittext@0.10.2 r-foreach@1.5.2 r-forcats@1.0.1 r-dplyr@1.1.4 r-dbarts@0.9-32 r-data-table@1.17.8 r-bartmachine@1.4.1.1 r-bart@2.9.10 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/ldongeunl/bartXViz
Licenses: GPL 2+
Build system: r
Synopsis: Visualization of BART and BARP using SHAP
Description:

Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>, this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. The BART model is designed to work with the following R packages: BART <doi:10.18637/jss.v097.i01>, bartMachine <doi:10.18637/jss.v070.i04>, and dbarts <https://CRAN.R-project.org/package=dbarts>. For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) <doi:10.1038/s42256-019-0138-9> is also considered. The BARP model proposed by Bisbee (2019) <doi:10.1017/S0003055419000480> was implemented with reference to <https://github.com/jbisbee1/BARP> and is designed to work with modified functions based on that implementation. BARP extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods.

r-biblionetwork 0.1.0
Propagated dependencies: r-rdpack@2.6.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/agoutsmedt/biblionetwork
Licenses: Expat
Build system: r
Synopsis: Create Different Types of Bibliometric Networks
Description:

This package provides functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) <doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008) <doi:10.1002/meet.2008.1450450292>.

r-bidser 0.2.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-stringdist@0.9.15 r-rlang@1.1.6 r-rio@1.2.4 r-readr@2.1.6 r-purrr@1.2.0 r-neuroim2@0.8.5 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-fs@1.6.6 r-dplyr@1.1.4 r-data-tree@1.2.0 r-crayon@1.5.3 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/bbuchsbaum/bidser
Licenses: Expat
Build system: r
Synopsis: Work with 'BIDS' (Brain Imaging Data Structure) Projects
Description:

This package provides tools for working with BIDS (Brain Imaging Data Structure) formatted neuroimaging datasets. The package provides functionality for reading and querying BIDS'-compliant projects, creating mock BIDS datasets for testing, and extracting preprocessed data from fMRIPrep derivatives. It supports searching and filtering BIDS files by various entities such as subject, session, task, and run to streamline neuroimaging data workflows. See Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44> for the BIDS specification.

r-betapass 1.1-2
Propagated dependencies: r-pbapply@1.7-4 r-ggplot2@4.0.1 r-betareg@3.2-4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BetaPASS
Licenses: GPL 2+
Build system: r
Synopsis: Calculate Power and Sample Size with Beta Regression
Description:

Power calculations are a critical component of any research study to determine the minimum sample size necessary to detect differences between multiple groups. Researchers often work with data taking the form of proportions that can be modeled with a beta distribution. Here we present an R package, BetaPASS', that perform power and sample size calculations for data following a beta distribution with comparative nonparametric output. This package allows flexibility with multiple options for link functions to fit the data and graphing functionality for visual comparisons.

r-bimets 4.1.2
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/andrea-luciani/bimets
Licenses: GPL 3
Build system: r
Synopsis: Time Series and Econometric Modeling
Description:

Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, rational expectations, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control, by A. Luciani (2022) <doi:10.13140/RG.2.2.31160.83202>.

r-bidask 2.1.5
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/eguidotti/bidask
Licenses: Expat
Build system: r
Synopsis: Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices
Description:

This package implements the efficient estimator of bid-ask spreads from open, high, low, and close prices described in Ardia, Guidotti, & Kroencke (JFE, 2024) <doi:10.1016/j.jfineco.2024.103916>. It also provides an implementation of the estimators described in Roll (JF, 1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>, Corwin & Schultz (JF, 2012) <doi:10.1111/j.1540-6261.2012.01729.x>, and Abdi & Ranaldo (RFS, 2017) <doi:10.1093/rfs/hhx084>.

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