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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-bruneimap 0.3.1
Propagated dependencies: r-sf@1.0-23 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://github.com/Bruneiverse/bruneimap
Licenses: GPL 3+
Build system: r
Synopsis: Maps and Spatial Data of Brunei
Description:

This package provides spatial data for mapping Brunei, including boundaries for districts, mukims, and kampongs, as well as locations of key infrastructure such as masjids, hospitals, clinics, and schools. The package supports researchers, analysts, and developers working with Bruneiâ s geographic and demographic data, offering a quick and accessible foundation for creating maps and conducting spatial studies.

r-biometryassist 1.4.0
Propagated dependencies: r-xml2@1.5.0 r-stringi@1.8.7 r-scales@1.4.0 r-rlang@1.1.6 r-pracma@2.4.6 r-patchwork@1.3.2 r-multcompview@0.1-10 r-lattice@0.22-7 r-ggplot2@4.0.1 r-emmeans@2.0.0 r-curl@7.0.0 r-askpass@1.2.1 r-agricolae@1.3-7
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://biometryhub.github.io/biometryassist/
Licenses: Expat
Build system: r
Synopsis: Functions to Assist Design and Analysis of Agronomic Experiments
Description:

This package provides functions to aid in the design and analysis of agronomic and agricultural experiments through easy access to documentation and helper functions, especially for users who are learning these concepts. While not required for most functionality, this package enhances the `asreml` package which provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased as asreml-R from VSNi <https://vsni.co.uk/>, who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>).

r-branchglm 3.0.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/JacobSeedorff21/BranchGLM
Licenses: FSDG-compatible
Build system: r
Synopsis: Efficient Best Subset Selection for GLMs via Branch and Bound Algorithms
Description:

This package performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in RcppArmadillo'. Parallel computation is available using OpenMP'.

r-bitfield 0.6.1
Propagated dependencies: r-yaml@2.3.10 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-terra@1.8-86 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-httr@1.4.7 r-glue@1.8.0 r-gitcreds@0.1.2 r-gh@1.5.0 r-dplyr@1.1.4 r-crayon@1.5.3 r-codetools@0.2-20 r-checkmate@2.3.3 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/bitfloat/bitfield
Licenses: GPL 3+
Build system: r
Synopsis: Handle Bitfields to Record Meta Data
Description:

Record algorithmic and analytic meta data along a workflow to store that in a bitfield, which can be published alongside any (modelled) data products.

r-bigleaf 0.8.2
Propagated dependencies: r-solartime@0.0.4 r-robustbase@0.99-6
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://bitbucket.org/juergenknauer/bigleaf
Licenses: GPL 2+
Build system: r
Synopsis: Physical and Physiological Ecosystem Properties from Eddy Covariance Data
Description:

Calculation of physical (e.g. aerodynamic conductance, surface temperature), and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. Calculations assume the land surface to behave like a big-leaf and return bulk ecosystem/canopy variables.

r-bayesmallowssmc2 0.2.1
Propagated dependencies: 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://cran.r-project.org/package=BayesMallowsSMC2
Licenses: GPL 3
Build system: r
Synopsis: Nested Sequential Monte Carlo for the Bayesian Mallows Model
Description:

This package provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model, which is a widely used probability model for rank and preference data. The package implements the SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially arriving rankings and pairwise preferences, including support for complete rankings, partial rankings, and pairwise comparisons. The methods are based on Sorensen (2025) <doi:10.1214/25-BA1564>.

r-bootur 1.0.4
Propagated dependencies: r-urca@1.3-4 r-rcppthread@2.2.0 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-parallelly@1.45.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/smeekes/bootUR
Licenses: GPL 2+
Build system: r
Synopsis: Bootstrap Unit Root Tests
Description:

Set of functions to perform various bootstrap unit root tests for both individual time series (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>, Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>, Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.

r-bayeswatch 0.1.4
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-mass@7.3-65 r-hotelling@1.0-8 r-gridextra@2.3 r-ggplot2@4.0.1 r-ess@1.1.2.1 r-cholwishart@1.1.4 r-bh@1.87.0-1 r-bdgraph@2.74
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bayesWatch
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Change-Point Detection for Process Monitoring with Fault Detection
Description:

Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023) <doi:10.48550/arXiv.2310.02940>.

r-bayessurv 3.8
Propagated dependencies: r-survival@3.8-3 r-smoothsurv@2.6 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://msekce.karlin.mff.cuni.cz/~komarek/
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Survival Regression with Flexible Error and Random Effects Distributions
Description:

This package contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models for censored data. Those can be not only right-censored but also interval-censored, doubly-interval-censored or misclassified interval-censored. The methods implemented in the package have been published in Komárek and Lesaffre (2006, Stat. Modelling) <doi:10.1191/1471082X06st107oa>, Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) <doi:10.1002/sim.3083>, Komárek and Lesaffre (2007, Stat. Sinica) <https://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n27.pdf>, Komárek and Lesaffre (2008, JASA) <doi:10.1198/016214507000000563>, Garcà a-Zattera, Jara and Komárek (2016, Biometrics) <doi:10.1111/biom.12424>.

r-bunching 0.8.6
Propagated dependencies: r-tidyr@1.3.1 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bb@2019.10-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/mavpanos/bunching
Licenses: Expat
Build system: r
Synopsis: Estimate Bunching
Description:

Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.

r-biogeom 1.5.0
Propagated dependencies: r-spatstat-geom@3.6-1 r-bmp@0.3.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=biogeom
Licenses: GPL 2+
Build system: r
Synopsis: Biological Geometries
Description:

Is used to simulate and fit biological geometries. biogeom incorporates several novel universal parametric equations that can generate the profiles of bird eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings (Gielis (2003) <doi:10.3732/ajb.90.3.333>; Shi et al. (2020) <doi:10.3390/sym12040645>), three growth-rate curves representing the ontogenetic growth trajectories of animals and plants against time, and the axially symmetrical and integral forms of all these functions (Shi et al. (2017) <doi:10.1016/j.ecolmodel.2017.01.012>; Shi et al. (2021) <doi:10.3390/sym13081524>). The optimization method proposed by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> was used to estimate model parameters. biogeom includes several real data sets of the boundary coordinates of natural shapes, including avian eggs, fruit, lanceolate and ovate leaves, tree rings, seeds, and sea stars,and can be potentially applied to other natural shapes. biogeom can quantify the conspecific or interspecific similarity of natural outlines, and provides information with important ecological and evolutionary implications for the growth and form of living organisms. Please see Shi et al. (2022) <doi:10.1111/nyas.14862> for details.

r-babynames 1.0.1
Propagated dependencies: r-tibble@3.3.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/hadley/babynames
Licenses: CC0
Build system: r
Synopsis: US Baby Names 1880-2017
Description:

US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex.

r-biom2 1.1.3
Propagated dependencies: r-wordcloud2@0.2.1 r-wgcna@1.73 r-webshot@0.5.5 r-viridis@0.6.5 r-uwot@0.2.4 r-rocr@1.0-11 r-mlr3verse@0.3.1 r-mlr3@1.2.0 r-igraph@2.2.1 r-htmlwidgets@1.6.4 r-ggthemes@5.1.0 r-ggstatsplot@0.13.3 r-ggsci@4.1.0 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ggnetwork@0.5.14 r-ggforce@0.5.0 r-cmplot@4.5.1 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=BioM2
Licenses: Expat
Build system: r
Synopsis: Biologically Explainable Machine Learning Framework
Description:

Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.

r-bmemlavaan 0.7
Propagated dependencies: r-snowfall@1.84-6.3 r-sem@3.1-16 r-rsem@0.5.1 r-mass@7.3-65 r-lavaan@0.6-20 r-amelia@1.8.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://bigdatalab.nd.edu
Licenses: GPL 2
Build system: r
Synopsis: Mediation Analysis with Missing Data and Non-Normal Data
Description:

This package provides methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014, <doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012, <doi:10.1007/s11336-012-9282-4>).

r-bsub 2.0.6
Propagated dependencies: r-ssh@0.9.4 r-igraph@2.2.1 r-globaloptions@0.1.2 r-getoptlong@1.0.5 r-digest@0.6.39 r-crayon@1.5.3 r-codetools@0.2-20 r-clisymbols@1.2.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jokergoo/bsub
Licenses: Expat
Build system: r
Synopsis: Submitter and Monitor of the 'LSF Cluster'
Description:

It submits R code/R scripts/shell commands to LSF cluster (<https://en.wikipedia.org/wiki/Platform_LSF>, the bsub system) without leaving R. There is also an interactive shiny application for monitoring job status.

r-bayessurvive 0.1.0
Propagated dependencies: r-testthat@3.3.0 r-survival@3.8-3 r-riskregression@2025.09.17 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-ggplot2@4.0.1 r-ggally@2.4.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/ocbe-uio/BayesSurvive
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Survival Models for High-Dimensional Data
Description:

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

r-bark 1.0.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://www.R-project.org
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Additive Regression Kernels
Description:

Bayesian Additive Regression Kernels (BARK) provides an implementation for non-parametric function estimation using Levy Random Field priors for functions that may be represented as a sum of additive multivariate kernels. Kernels are located at every data point as in Support Vector Machines, however, coefficients may be heavily shrunk to zero under the Cauchy process prior, or even, set to zero. The number of active features is controlled by priors on precision parameters within the kernels, permitting feature selection. For more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels", Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 <doi:10.1214/11-AOS889>.

r-biomor 0.1.1
Propagated dependencies: r-xgboost@1.7.11.1 r-themis@1.0.3 r-recipes@1.3.1 r-proc@1.19.0.1 r-magrittr@2.0.4 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=BioMoR
Licenses: Expat
Build system: r
Synopsis: Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble
Description:

This package provides tools for bioinformatics modeling using recursive transformer-inspired architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. Includes utilities for cross-validation, calibration, benchmarking, and threshold optimization in predictive modeling workflows. The methodology builds on ensemble learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov 2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).

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-baclava 1.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rcppnumerical@0.6-0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 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=baclava
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Analysis of Cancer Latency with Auxiliary Variable Augmentation
Description:

This package provides a novel data-augmentation Markov chain Monte Carlo sampling algorithm to fit a progressive compartmental model of disease in a Bayesian framework Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024) <doi:10.48550/arXiv.2408.14625>.

r-bartman 0.2.1
Propagated dependencies: r-tidyr@1.3.1 r-tidygraph@1.3.1 r-scales@1.4.0 r-rrapply@1.2.8 r-rlang@1.1.6 r-rjava@1.0-11 r-purrr@1.2.0 r-patchwork@1.3.2 r-gtable@0.3.6 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-ggiraph@0.9.2 r-dplyr@1.1.4 r-dendser@1.0.2 r-dbarts@0.9-32 r-cowplot@1.2.0 r-colorspace@2.1-2 r-bartmachine@1.4.1.1 r-bart@2.9.10
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bartMan
Licenses: GPL 2+
Build system: r
Synopsis: Create Visualisations for BART Models
Description:

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits. We construct conventional plots to analyze a modelâ s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely BART Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>, dbarts (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>, and bartMachine (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.

r-blockcv 3.2-0
Propagated dependencies: r-terra@1.8-86 r-sp@2.2-0 r-sf@1.0-23 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-cowplot@1.2.0 r-automap@1.1-20
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/rvalavi/blockCV
Licenses: GPL 3+
Build system: r
Synopsis: Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation
Description:

Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.

r-bigqf 1.6
Propagated dependencies: r-svd@0.5.8 r-matrix@1.7-4 r-coxme@2.2-22 r-compquadform@1.4.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/tslumley/bigQF
Licenses: GPL 2
Build system: r
Synopsis: Quadratic Forms in Large Matrices
Description:

This package provides a computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

r-bin2norm 0.1.0
Propagated dependencies: r-statmod@1.5.1 r-rstan@2.32.7 r-lme4@1.1-37
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bin2norm
Licenses: Expat
Build system: r
Synopsis: Hierarchical Probit Estimation for Dichotomized Data
Description:

This package provides likelihood-based and hierarchical estimation methods for thresholded (binomial-probit) data. Supports fixed-mean and random-mean models with maximum likelihood estimation (MLE), generalized linear mixed model (GLMM), and Bayesian Markov chain Monte Carlo (MCMC) implementations. For methodological background, see Albert and Chib (1993) <doi:10.1080/01621459.1993.10476321> and McCulloch (1994) <doi:10.2307/2297959>.

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Total results: 68658