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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-bgdata 2.4.1
Propagated dependencies: r-synchronicity@1.3.10 r-symdmatrix@2.1.1 r-linkedmatrix@1.4.0 r-ff@4.5.2 r-crochet@2.3.0 r-bit@4.6.0 r-bigmemory@4.6.4 r-bedmatrix@2.0.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/QuantGen/BGData
Licenses: Expat
Build system: r
Synopsis: Suite of Packages for Analysis of Big Genomic Data
Description:

An umbrella package providing a phenotype/genotype data structure and scalable and efficient computational methods for large genomic datasets in combination with several other packages: BEDMatrix', LinkedMatrix', and symDMatrix'.

r-bridger2 0.1.0
Propagated dependencies: r-shinydashboard@0.7.3 r-shiny@1.11.1 r-plotly@4.11.0 r-outliers@0.15 r-ggplot2@4.0.1 r-data-table@1.17.8 r-bsda@1.2.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bridger2
Licenses: Expat
Build system: r
Synopsis: Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data
Description:

BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells. This package provides a series of functions for performing quality check of your BRIC-seq data, calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.

r-blr 1.6
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BLR
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Linear Regression
Description:

Bayesian Linear Regression.

r-babette 2.3.4
Propagated dependencies: r-xml2@1.5.0 r-tracerer@2.2.4 r-stringr@1.6.0 r-rlang@1.1.6 r-phangorn@2.12.1 r-mauricer@2.5.4 r-beautier@2.6.12 r-beastier@2.5.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://docs.ropensci.org/babette/
Licenses: GPL 3
Build system: r
Synopsis: Control '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 commonly accompanied by BEAUti 2', Tracer and DensiTree'. babette provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from R'.

r-binordnonnor 1.5.2
Propagated dependencies: r-ordnor@2.2.3 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-genord@2.0.0 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=BinOrdNonNor
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Concurrent Generation of Binary, Ordinal and Continuous Data
Description:

Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

r-bossr 1.0.4
Propagated dependencies: r-survival@3.8-3 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bossR
Licenses: GPL 3
Build system: r
Synopsis: Biomarker Optimal Segmentation System
Description:

The Biomarker Optimal Segmentation System R package, bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that bossR offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.

r-bssoverspace 0.1.0
Propagated dependencies: r-spatialbss@0.16-0 r-rspde@2.5.2 r-expm@1.0-0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BSSoverSpace
Licenses: GPL 3
Build system: r
Synopsis: Blind Source Separation for Multivariate Spatial Data using Eigen Analysis
Description:

This package provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. BSSoverSpace is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.

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-bpnreg 2.0.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-haven@2.5.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/joliencremers/bpnreg
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Projected Normal Regression Models for Circular Data
Description:

Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) <doi:10.1016/j.csda.2012.07.025>.

r-bcmixed 0.1.5
Propagated dependencies: r-nlme@3.1-168 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=bcmixed
Licenses: GPL 2+
Build system: r
Synopsis: Mixed Effect Model with the Box-Cox Transformation
Description:

Inference on the marginal model of the mixed effect model with the Box-Cox transformation and on the model median differences between treatment groups for longitudinal randomized clinical trials. These statistical methods are proposed by Maruo et al. (2017) <doi:10.1002/sim.7279>.

r-bayesmultimode 0.7.5
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-sn@2.1.1 r-rdpack@2.6.4 r-posterior@1.6.1 r-mvtnorm@1.3-3 r-mcmcglmm@2.36 r-magrittr@2.0.4 r-gtools@3.9.5 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bayesplot@1.14.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/paullabonne/BayesMultiMode
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Mode Inference
Description:

This package provides a two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>). First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference.

r-beeca 0.2.0
Propagated dependencies: r-sandwich@3.1-1 r-lifecycle@1.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://openpharma.github.io/beeca/
Licenses: LGPL 3+
Build system: r
Synopsis: Binary Endpoint Estimation with Covariate Adjustment
Description:

This package performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

r-bayesdfa 1.3.4
Propagated dependencies: r-viridislite@0.4.2 r-stanheaders@2.32.10 r-rstan@2.32.7 r-rlang@1.1.6 r-reshape2@1.4.5 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-mgcv@1.9-4 r-loo@2.8.0 r-ggplot2@4.0.1 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://fate-ewi.github.io/bayesdfa/
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Description:

This package implements Bayesian dynamic factor analysis with Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. bayesdfa extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.

r-bipl5 1.0.2
Propagated dependencies: r-plotly@4.11.0 r-knitr@1.50 r-htmlwidgets@1.6.4 r-crayon@1.5.3 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bipl5
Licenses: Expat
Build system: r
Synopsis: Construct Reactive Calibrated Axes Biplots
Description:

This package provides a modern view on the principal component analysis biplot with calibrated axes. Create principal component analysis biplots rendered in HTML with significant reactivity embedded within the plot. Furthermore, the traditional biplot view is enhanced by translated axes with inter-class kernel densities superimposed. For more information on biplots, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0).

r-bootct 2.1.0
Propagated dependencies: r-vars@1.6-1 r-usethis@3.2.1 r-urca@1.3-4 r-stringr@1.6.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pracma@2.4.6 r-magrittr@2.0.4 r-gtools@3.9.5 r-dynamac@0.1.12 r-dplyr@1.1.4 r-ardl@0.2.4 r-aod@1.3.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bootCT
Licenses: GPL 2+
Build system: r
Synopsis: Bootstrapping the ARDL Tests for Cointegration
Description:

The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - <doi:10.1002/jae.616>) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.

r-bayesmixsurv 0.9.3
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesMixSurv
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and Stratification
Description:

Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components in the hazard function.

r-businessduration 0.2.0
Propagated dependencies: r-chron@2.3-62
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BusinessDuration
Licenses: AGPL 3
Build system: r
Synopsis: Calculates Business Duration Between Two Dates
Description:

Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours.

r-betadelta 1.0.6
Propagated dependencies: r-numderiv@2016.8-1.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jeksterslab/betaDelta
Licenses: Expat
Build system: r
Synopsis: Confidence Intervals for Standardized Regression Coefficients
Description:

Generates confidence intervals for standardized regression coefficients using delta method standard errors for models fitted by lm() as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6> and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>. The package can also be used to generate confidence intervals for differences of standardized regression coefficients and as a general approach to performing the delta method. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

r-bdesize 1.6
Propagated dependencies: r-ggplot2@4.0.1 r-fpow@0.0-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BDEsize
Licenses: GPL 2+
Build system: r
Synopsis: Efficient Determination of Sample Size in Balanced Design of Experiments
Description:

For a balanced design of experiments, this package calculates the sample size required to detect a certain standardized effect size, under a significance level. This package also provides three graphs; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power, which show the mutual relationship between the sample size, power and the detectable standardized effect size. The detailed procedure is described in R. V. Lenth (2006-9) <https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) <doi:10.2307/2334851>, and Douglas C. Montgomery (2013, ISBN: 0849323312).

r-bfboin 0.1.1
Propagated dependencies: r-purrr@1.2.0 r-boin@2.7.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://openpharma.github.io/bfboin/
Licenses: GPL 3+
Build system: r
Synopsis: Operating Characteristics for the Bayesian Optimal Interval Design with Back Filling
Description:

Calculate the operating characteristics of the Bayesian Optimal Interval with Back Filling Design for dose escalation in early-phase oncology trials.

r-binr 1.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jabiru/binr
Licenses: ASL 2.0
Build system: r
Synopsis: Cut Numeric Values into Evenly Distributed Groups
Description:

Package binr (pronounced as "binner") provides algorithms for cutting numerical values exhibiting a potentially highly skewed distribution into evenly distributed groups (bins). This functionality can be applied for binning discrete values, such as counts, as well as for discretization of continuous values, for example, during generation of features used in machine learning algorithms.

r-bootstepaic 1.4-0
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=bootStepAIC
Licenses: GPL 2+
Build system: r
Synopsis: Bootstrap stepAIC
Description:

Model selection by bootstrapping the stepAIC() procedure.

r-bssasymp 1.2-4
Propagated dependencies: r-jade@2.0-4 r-fica@1.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BSSasymp
Licenses: GPL 2+
Build system: r
Synopsis: Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing Matrix Estimates
Description:

This package provides functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.

r-boxcoxmix 0.46
Propagated dependencies: r-statmod@1.5.1 r-qicharts@0.5.10 r-npmlreg@0.46-5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://gitlab.com/iagogv/boxcoxmix
Licenses: GPL 3+
Build system: r
Synopsis: Box-Cox-Type Transformations for Linear and Logistic Models with Random Effects
Description:

Box-Cox-type transformations for linear and logistic models with random effects using non-parametric profile maximum likelihood estimation, as introduced in Almohaimeed (2018) <http://etheses.dur.ac.uk/12831/> and Almohaimeed and Einbeck (2022) <doi:10.1177/1471082X20966919>. The main functions are optim.boxcox() for linear models with random effects and boxcoxtype() for logistic models with random effects.

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