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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-bliss 1.1.1
Propagated dependencies: r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mass@7.3-65 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/pmgrollemund/bliss
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Functional Linear Regression with Sparse Step Functions
Description:

This package provides a method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.

r-brms-mmrm 1.1.1
Propagated dependencies: r-zoo@1.8-14 r-trialr@0.1.6 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-posterior@1.6.1 r-mass@7.3-65 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-brms@2.23.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://openpharma.github.io/brms.mmrm/
Licenses: Expat
Build system: r
Synopsis: Bayesian MMRMs using 'brms'
Description:

The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and brms is a powerful and versatile package for fitting Bayesian regression models. The brms.mmrm R package leverages brms to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.

r-beastt 0.0.3
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-stanheaders@2.32.10 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-purrr@1.2.0 r-mixtools@2.0.0.1 r-ggplot2@4.0.1 r-ggdist@3.3.3 r-generics@0.1.4 r-dplyr@1.1.4 r-distributional@0.5.0 r-cobalt@4.6.2 r-cli@3.6.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://gsk-biostatistics.github.io/beastt/
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials
Description:

Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.

r-bbw 0.3.1
Propagated dependencies: r-withr@3.0.2 r-stringr@1.6.0 r-parallelly@1.45.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-cli@3.6.5 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/rapidsurveys/bbw
Licenses: GPL 3
Build system: r
Synopsis: Blocked Weighted Bootstrap
Description:

The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population-proportional sampling or PPS as used in Standardized Monitoring and Assessment of Relief and Transitions or SMART surveys) or posterior weighting (e.g. as used in rapid assessment method or RAM and simple spatial sampling method or S3M surveys) is implemented. See Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys.

r-bayessampling 1.1.0
Propagated dependencies: r-matrixcalc@1.0-6 r-matrix@1.7-4 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886
Licenses: GPL 3
Build system: r
Synopsis: Bayes Linear Estimators for Finite Population
Description:

Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.

r-bumbl 1.0.4
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-purrr@1.2.0 r-mass@7.3-65 r-lifecycle@1.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Aariq/bumbl
Licenses: Expat
Build system: r
Synopsis: Tools for Modeling Bumblebee Colony Growth and Decline
Description:

Bumblebee colonies grow during worker production, then decline after switching to production of reproductive individuals (drones and gynes). This package provides tools for modeling and visualizing this pattern by identifying a switchpoint with a growth rate before and a decline rate after the switchpoint. The mathematical models fit by bumbl are described in Crone and Williams (2016) <doi:10.1111/ele.12581>.

r-bart 2.9.10
Propagated dependencies: r-survival@3.8-3 r-rcpp@1.1.0 r-nlme@3.1-168
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BART
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Additive Regression Trees
Description:

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

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-boxly 0.1.1
Propagated dependencies: r-uuid@1.2-1 r-rlang@1.1.6 r-plotly@4.11.0 r-metalite@0.1.4 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-ggplot2@4.0.1 r-dt@0.34.0 r-crosstalk@1.2.2 r-brew@1.0-10
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://merck.github.io/boxly/
Licenses: GPL 3+
Build system: r
Synopsis: Interactive Box Plot
Description:

Interactive box plot using plotly for clinical trial analysis.

r-bayesestdft 1.0.0
Propagated dependencies: r-numderiv@2016.8-1.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Roy-SR-007/bayesestdft
Licenses: Expat
Build system: r
Synopsis: Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework
Description:

This package provides a Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in <doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior.

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-betanb 1.0.6
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jeksterslab/betaNB
Licenses: Expat
Build system: r
Synopsis: Bootstrap for Regression Effect Sizes
Description:

Generates nonparametric bootstrap confidence intervals (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().

r-btdecaylasso 0.1.1
Propagated dependencies: r-optimx@2025-4.9 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=BTdecayLasso
Licenses: GPL 2+
Build system: r
Synopsis: Bradley-Terry Model with Exponential Time Decayed Log-Likelihood and Adaptive Lasso
Description:

We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.

r-blapsr 0.7.0
Propagated dependencies: r-survival@3.8-3 r-sn@2.1.1 r-rspectra@0.16-2 r-matrix@1.7-4 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://github.com/oswaldogressani/blapsr>
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Inference with Laplace Approximations and P-Splines
Description:

Laplace approximations and penalized B-splines are combined for fast Bayesian inference in latent Gaussian models. The routines can be used to fit survival models, especially proportional hazards and promotion time cure models (Gressani, O. and Lambert, P. (2018) <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also be implemented for inference in (generalized) additive models (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>). See the associated website for more information and examples.

r-bretigea 1.0.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BRETIGEA
Licenses: Expat
Build system: r
Synopsis: Brain Cell Type Specific Gene Expression Analysis
Description:

Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the CellCODE R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.

r-bcee 1.3.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-leaps@3.2 r-boot@1.3-32 r-bma@3.18.20
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BCEE
Licenses: GPL 2+
Build system: r
Synopsis: The Bayesian Causal Effect Estimation Algorithm
Description:

This package provides a Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) <doi:10.1515/jci-2014-0035> Talbot and Beaudoin (2022) <doi:10.1515/jci-2021-0023>.

r-brainr 1.7.0
Propagated dependencies: r-rgl@1.3.31 r-oro-nifti@0.11.4 r-misc3d@0.9-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=brainR
Licenses: GPL 2
Build system: r
Synopsis: Helper Functions to 'misc3d' and 'rgl' Packages for Brain Imaging
Description:

This includes functions for creating 3D and 4D images using WebGL', rgl', and JavaScript commands. This package relies on the X toolkit ('XTK', <https://github.com/xtk/X#readme>).

r-binaryrl 0.9.9
Propagated dependencies: r-rcpp@1.1.0 r-progressr@0.18.0 r-future@1.68.0 r-foreach@1.5.2 r-dorng@1.8.6.2 r-dofuture@1.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://yuki-961004.github.io/binaryRL/
Licenses: GPL 3
Build system: r
Synopsis: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks
Description:

This package provides tools for building Rescorla-Wagner Models for Two-Alternative Forced Choice tasks, commonly employed in psychological research. Most concepts and ideas within this R package are referenced from Sutton and Barto (2018) <ISBN:9780262039246>. The package allows for the intuitive definition of RL models using simple if-else statements and three basic models built into this R package are referenced from Niv et al. (2012) <doi:10.1523/JNEUROSCI.5498-10.2012>. Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Example datasets included with the package are sourced from the work of Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.

r-biggp 0.1.9
Propagated dependencies: r-rmpi@0.7-3.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://doi.org/10.18637/jss.v063.i10
Licenses: GPL 2+
Build system: r
Synopsis: Distributed Gaussian Process Calculations
Description:

Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. The bigGP class provides high-level methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations), and simulation of realizations. In addition, bigGP provides an API for basic matrix calculations with distributed covariance matrices, including Cholesky decomposition, back/forwardsolve, crossproduct, and matrix multiplication.

r-bdpv 1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bdpv
Licenses: GPL 2+
Build system: r
Synopsis: Inference and Design for Predictive Values in Diagnostic Tests
Description:

Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.

r-biocro 3.3.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/biocro/biocro
Licenses: Expat
Build system: r
Synopsis: Modular Crop Growth Simulations
Description:

This package provides a cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.

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-bigtime 0.2.3
Propagated dependencies: r-tidyr@1.3.1 r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-corrplot@0.95
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/ineswilms/bigtime
Licenses: GPL 2+
Build system: r
Synopsis: Sparse Estimation of Large Time Series Models
Description:

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

r-bpdir 0.1.2
Propagated dependencies: r-plotrix@3.8-13 r-mass@7.3-65 r-circular@0.5-2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bpDir
Licenses: GPL 2
Build system: r
Synopsis: Boxplots for Directional Data
Description:

This package provides functions for drawing boxplots for data on (the boundary of) a unit circle (i.e., circular and axial data), from Buttarazzi D., Pandolfo G., Porzio G.C. (2018) <doi:10.1111/biom.12889>.

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Total results: 68658