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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 search send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-bsims 0.3-3
Propagated dependencies: r-pbapply@1.7-4 r-mefa4@0.3-12 r-mass@7.3-65 r-intrval@1.0-0 r-deldir@2.0-4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/psolymos/bSims
Licenses: GPL 2
Build system: r
Synopsis: Agent-Based Bird Point Count Simulator
Description:

This package provides a highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 <doi:10.1007/s42977-023-00183-2>). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>, Solymos et al. (2013) <doi:10.1111/2041-210X.12106>, and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>, and sound attenuation experiments by Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.

r-biganalytics 1.1.22
Propagated dependencies: r-rcpp@1.1.1-1.1 r-foreach@1.5.2 r-bigmemory@4.6.4 r-biglm@0.9-3 r-bh@1.90.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://www.bigmemory.org
Licenses: LGPL 3 ASL 2.0
Build system: r
Synopsis: Utilities for 'big.matrix' Objects from Package 'bigmemory'
Description:

Extend the bigmemory package with various analytics. Functions bigkmeans and binit may also be used with native R objects. For tapply'-like functions, the bigtabulate package may also be helpful. For linear algebra support, see bigalgebra'. For mutex (locking) support for advanced shared-memory usage, see synchronicity'.

r-bidsr 0.1.1
Propagated dependencies: r-uuid@1.2-2 r-s7@0.2.2 r-nanotime@0.3.15 r-jsonlite@2.0.0 r-fs@2.1.0 r-fastmap@1.2.0 r-data-table@1.18.4 r-checkmate@2.3.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://dipterix.org/bidsr/
Licenses: Expat
Build system: r
Synopsis: Brain Imaging Data Structure ('BIDS') Parser
Description:

Parse and read the files that comply with the brain imaging data structure, or BIDS format, see the publication from Gorgolewski, K., Auer, T., Calhoun, V. et al. (2016) <doi:10.1038/sdata.2016.44>. Provides query functions to extract and check the BIDS entity information (such as subject, session, task, etc.) from the file paths and suffixes according to the specification. The package is developed and used in the reproducible analysis and visualization of intracranial electroencephalography, or RAVE', see Magnotti, J. F., Wang, Z., and Beauchamp, M. S. (2020) <doi:10.1016/j.neuroimage.2020.117341>; see citation("bidsr") for details and attributions.

r-boostmath 1.4.0
Propagated dependencies: r-cpp11@0.5.5 r-bh@1.90.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/andrjohns/boostmath
Licenses: Expat
Build system: r
Synopsis: 'R' Bindings for the 'Boost' Math Functions
Description:

R bindings for the various functions and statistical distributions provided by the Boost Math library <https://www.boost.org/doc/libs/latest/libs/math/doc/html/index.html>.

r-bruneimap 0.3.1
Propagated dependencies: r-sf@1.1-1 r-lifecycle@1.0.5 r-dplyr@1.2.1
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-balancecheck 0.2
Propagated dependencies: r-mvtnorm@1.3-7 r-ade4@1.7-24
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-bintools 0.2.0
Propagated dependencies: r-tibble@3.3.1 r-stringi@1.8.7 r-stanheaders@2.32.10 r-rstantools@2.6.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-2 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.1-1.1 r-mvtnorm@1.3-7 r-dplyr@1.2.1 r-combinat@0.0-8 r-bh@1.90.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BINtools
Licenses: GPL 3
Build system: r
Synopsis: Bayesian BIN (Bias, Information, Noise) Model of Forecasting
Description:

This package provides a recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.

r-bayespiecehazselect 1.1.0
Propagated dependencies: r-mvtnorm@1.3-7
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesPieceHazSelect
Licenses: GPL 2
Build system: r
Synopsis: Variable Selection in a Hierarchical Bayesian Model for a Hazard Function
Description:

Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary and also uses Stochastic Search Variable Selection to determine what covariates drive the risk of the event. This function outputs trace plots depicting the number of split points in the hazard and the number of variables included in the hazard. The function saves all posterior quantities to the desired path.

r-bidag 2.1.4
Propagated dependencies: r-rgraphviz@2.56.0 r-rcpp@1.1.1-1.1 r-rbgl@1.88.0 r-pcalg@2.7-12 r-matrix@1.7-5 r-graph@1.90.0 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=BiDAG
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Inference for Directed Acyclic Graphs
Description:

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

r-bdesize 1.6
Propagated dependencies: r-ggplot2@4.0.3 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-biorad 0.12.0
Propagated dependencies: r-xml2@1.5.2 r-viridislite@0.4.3 r-viridis@0.6.5 r-tidyselect@1.2.1 r-tidyr@1.3.2 r-terra@1.9-27 r-suntools@1.1.0 r-stringr@1.6.0 r-sp@2.2-1 r-sf@1.1-1 r-rlang@1.2.0 r-rhdf5@2.56.0 r-readr@2.2.0 r-raster@3.6-32 r-lutz@0.3.2 r-lubridate@1.9.5 r-lifecycle@1.0.5 r-jsonlite@2.0.0 r-httr2@1.2.2 r-glue@1.8.1 r-ggplot2@4.0.3 r-fields@17.3 r-dplyr@1.2.1 r-curl@7.1.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/adokter/bioRad/
Licenses: Expat
Build system: r
Synopsis: Biological Analysis and Visualization of Weather Radar Data
Description:

Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028> for a software paper describing package and methodologies.

r-barborgradient 1.0.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BarBorGradient
Licenses: GPL 3
Build system: r
Synopsis: Function Minimum Approximator
Description:

Tool to find where a function has its lowest value(minimum). The functions can be any dimensions. Recommended use is with eps=10^-10, but can be run with 10^-20, although this depends on the function. Two more methods are in this package, simple gradient method (Gradmod) and Powell method (Powell). These are not recommended for use, their purpose are purely for comparison.

r-bioacoustics 0.2.10
Dependencies: fftw@3.3.10
Propagated dependencies: r-tuner@1.4.7 r-stringr@1.6.0 r-rcpp@1.1.1-1.1 r-moments@0.14.1 r-htmltools@0.5.9
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/wavx/bioacoustics/
Licenses: GPL 3
Build system: r
Synopsis: Analyse Audio Recordings and Automatically Extract Animal Vocalizations
Description:

This package contains all the necessary tools to process audio recordings of various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, display audio signals, detect and extract automatically acoustic features for further analysis such as classification.

r-bsl 3.2.5
Propagated dependencies: r-whitening@1.4.0 r-stringr@1.6.0 r-rdpack@2.6.6 r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1 r-mvtnorm@1.3-7 r-mass@7.3-65 r-gridextra@2.3 r-glasso@1.11 r-ggplot2@4.0.3 r-foreach@1.5.2 r-dorng@1.8.6.3 r-copula@1.1-7 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=BSL
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Synthetic Likelihood
Description:

Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). uBSL (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 <arXiv:1904.04551>) estimates the Gaussian synthetic likelihood whilst acknowledging that there may be incompatibility between the model and the observed summary statistic. Shrinkage estimation can help to decrease the number of model simulations when the dimension of the summary statistic is high (e.g., BSLasso, An et al. (2019) <doi:10.1080/10618600.2018.1537928>). Extensions to this package are planned. For a journal article describing how to use this package, see An et al. (2022) <doi:10.18637/jss.v101.i11>.

r-bayespim 1.0.1
Propagated dependencies: r-rcpp@1.1.1-1.1 r-mvtnorm@1.3-7 r-mass@7.3-65 r-ggamma@1.0.2 r-foreach@1.5.2 r-doparallel@1.0.17 r-coda@0.19-4.1 r-actuar@3.3-7
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/thomasklausch2/bayespim
Licenses: Expat
Build system: r
Synopsis: Bayesian Prevalence-Incidence Mixture Model
Description:

Models time-to-event data from interval-censored screening studies. It accounts for latent prevalence at baseline and incorporates misclassification due to imperfect test sensitivity. For usage details, see the package vignette "BayesPIM_intro". Further details can be found in Klausch, Lissenberg-Witte and Coupé (2026) <doi:10.1002/sim.70433>.

r-bayesregdtr 1.1.2
Propagated dependencies: r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1 r-progressr@0.19.0 r-mvtnorm@1.3-7 r-future@1.70.0 r-foreach@1.5.2 r-dorng@1.8.6.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jlimrasc/BayesRegDTR
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Regression for Dynamic Treatment Regimes
Description:

This package provides methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) <doi:10.1093/jrsssb/qkad016> Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing.

r-bglr 1.1.4
Propagated dependencies: r-truncnorm@1.0-9 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=BGLR
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Generalized Linear Regression
Description:

Bayesian Generalized Linear Regression.

r-bwgr 2.2.17
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.1-1.1 r-matrix@1.7-5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bWGR
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Whole-Genome Regression
Description:

Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).

r-brmsmargins 0.3.0
Propagated dependencies: r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1 r-posterior@1.7.0 r-extraoperators@0.4.0 r-data-table@1.18.4 r-brms@2.23.0 r-bayestestr@0.18.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://joshuawiley.com/brmsmargins/
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Marginal Effects for 'brms' Models
Description:

Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the brms package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).

r-burgle 0.1.2
Propagated dependencies: r-survival@3.8-6 r-riskregression@2026.03.11 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=burgle
Licenses: Expat
Build system: r
Synopsis: 'Burgle': Stealing the Necessary Parts of Model Objects
Description:

This package provides a way to reduce model objects to necessary parts, making them easier to work with, store, share and simulate multiple values for new responses while allowing for parameter uncertainty.

r-binaryemvs 0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BinaryEMVS
Licenses: GPL 3
Build system: r
Synopsis: Variable Selection for Binary Data Using the EM Algorithm
Description:

This package implements variable selection for high dimensional datasets with a binary response variable using the EM algorithm. Both probit and logit models are supported. Also included is a useful function to generate high dimensional data with correlated variables.

r-bsagri 0.1-10
Propagated dependencies: r-mvtnorm@1.3-7 r-multcomp@1.4-30 r-mratios@1.4.4 r-mcpan@1.1-22 r-gamlss@5.5-0 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BSagri
Licenses: GPL 2
Build system: r
Synopsis: Safety Assessment in Agricultural Field Trials
Description:

Collection of functions, data sets and code examples for evaluations of field trials with the objective of equivalence assessment.

r-btergm 1.11.1
Propagated dependencies: r-statnet-common@4.13.0 r-sna@2.8 r-rocr@1.0-12 r-network@1.20.0 r-matrix@1.7-5 r-igraph@2.3.1 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-betastability 0.0.4
Propagated dependencies: r-xgboost@3.2.1.1 r-vegan@2.7-3 r-usedist@0.4.0 r-reshape2@1.4.5 r-randomforest@4.7-1.2 r-mgcv@1.9-4 r-glmnet@5.0 r-ggplot2@4.0.3 r-gdm@1.6.0-7 r-bbmisc@1.13.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/gaoyu19920914/betaStability/
Licenses: Expat
Build system: r
Synopsis: Quantify the Compositional Stability of Each Community Based on a Single Sampling Event
Description:

Quantify the stability of each community based on the beta diversity between communities gathered in a single sampling event rather than a series of continuous sampling activities.

Total packages: 72463