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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-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+
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-bdlim 0.5.0
Propagated dependencies: r-laplacesdemon@16.1.6 r-ggplot2@4.0.1 r-bayeslogit@2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://anderwilson.github.io/bdlim/
Licenses: GPL 3+
Synopsis: Bayesian Distributed Lag Interaction Models
Description:

Estimation and interpretation of Bayesian distributed lag interaction models (BDLIMs). A BDLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a categorical variable under four specific patterns of modification. The main function is bdlim(). There are also summary and plotting files. Details on methodology are described in Wilson et al. (2017) <doi:10.1093/biostatistics/kxx002>.

r-bsw 0.1.2
Propagated dependencies: r-quadprog@1.5-8 r-matrixstats@1.5.0 r-matrix@1.7-4 r-checkmate@2.3.3 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/UdS-MF-IMBEI/BSW
Licenses: GPL 3+
Synopsis: Fitting a Log-Binomial Model Using the Bekhit–Schöpe–Wagenpfeil (BSW) Algorithm
Description:

This package implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.

r-brikmeans 1.0
Propagated dependencies: r-splines2@0.5.4 r-depthtools@0.7 r-cluster@2.1.8.1 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=briKmeans
Licenses: GPL 3+
Synopsis: Package for Brik, Fabrik and Fdebrik Algorithms to Initialise Kmeans
Description:

Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) <doi:10.1007/s00357-020-09372-3> It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al.

r-bootsurv 0.0.1
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=bootsurv
Licenses: GPL 3
Synopsis: Bootstrap Methods for Complete Survey Data
Description:

Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis.

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
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-bayes4psy 1.2.13
Propagated dependencies: r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-reshape@0.8.10 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-metrology@0.9-29-2 r-mcmcse@1.5-1 r-ggplot2@4.0.1 r-emg@1.0.9 r-dplyr@1.1.4 r-cowplot@1.2.0 r-circular@0.5-2 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/bstatcomp/bayes4psy
Licenses: GPL 3+
Synopsis: User Friendly Bayesian Data Analysis for Psychology
Description:

This package contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).

r-bioacoustics 0.2.10
Dependencies: fftw@3.3.10
Propagated dependencies: r-tuner@1.4.7 r-stringr@1.6.0 r-rcpp@1.1.0 r-moments@0.14.1 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/wavx/bioacoustics/
Licenses: GPL 3
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-bacct 1.0
Propagated dependencies: r-rjags@4-17 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=BACCT
Licenses: GPL 3+
Synopsis: Bayesian Augmented Control for Clinical Trials
Description:

This package implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling Just Another Gibbs Sampler ('JAGS') software. In addition, the BACCT package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have JAGS 4.0.0 or newer installed due to a compatibility issue with rjags package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the BACCT package.

r-blockmodelinggui 1.8.4
Propagated dependencies: r-visnetwork@2.1.4 r-shinythemes@1.2.0 r-shinyjs@2.1.0 r-shinybusy@0.3.3 r-shiny@1.11.1 r-network@1.19.0 r-intergraph@2.0-4 r-igraph@2.2.1 r-htmlwidgets@1.6.4 r-dt@0.34.0 r-blockmodeling@1.1.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BlockmodelingGUI
Licenses: GPL 3+
Synopsis: GUI for the Generalised Blockmodeling of Valued Networks
Description:

This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

r-blpestimator 0.3.4
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-randtoolbox@2.0.5 r-numderiv@2016.8-1.1 r-mvquad@1.0-8 r-matrix@1.7-4 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BLPestimatoR
Licenses: GPL 3
Synopsis: Performs a BLP Demand Estimation
Description:

This package provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.

r-bayesvarsel 2.4.5
Propagated dependencies: r-mvtnorm@1.3-3 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/comodin19/BayesVarSel
Licenses: GPL 2
Synopsis: Bayes Factors, Model Choice and Variable Selection in Linear Models
Description:

Bayes factors and posterior probabilities in Linear models, aimed at provide a formal Bayesian answer to testing and variable selection problems.

r-baggingbwsel 1.1
Propagated dependencies: r-sm@2.2-6.0 r-rcpp@1.1.0 r-nor1mix@1.3-3 r-misc3d@0.9-1 r-mclust@6.1.2 r-kedd@1.0.4 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://rubenfcasal.github.io/baggingbwsel/
Licenses: GPL 3
Synopsis: Bagging Bandwidth Selection in Kernel Density and Regression Estimation
Description:

Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and Barreiro-Ures et al. (2021) <doi:10.48550/arXiv.2105.04134>.

r-bioprobability 1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BioProbability
Licenses: GPL 2
Synopsis: Probability in Biostatistics
Description:

Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided.

r-bnns 0.1.2
Propagated dependencies: r-rstan@2.32.7 r-rcppeigen@0.3.4.0.2 r-proc@1.19.0.1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/swarnendu-stat/bnns
Licenses: Expat
Synopsis: Bayesian Neural Network with 'Stan'
Description:

Offers a flexible formula-based interface for building and training Bayesian Neural Networks powered by Stan'. The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) <doi:10.1007/978-1-4612-0745-0>.

r-bayespo 0.5.0
Propagated dependencies: r-rcppprogress@0.4.2 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 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=bayesPO
Licenses: GPL 3
Synopsis: Bayesian Inference for Presence-Only Data
Description:

Presence-Only data is best modelled with a Point Process Model. The work of Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides a way to use exact Bayesian inference to model this type of data, which is implemented in this package.

r-brolgar 1.0.2
Propagated dependencies: r-vctrs@0.6.5 r-tsibble@1.1.6 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-fabletools@0.5.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/njtierney/brolgar
Licenses: Expat
Synopsis: Browse Over Longitudinal Data Graphically and Analytically in R
Description:

This package provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the tsibble package, and is designed to integrate within the tidyverse', and tidyverts (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in ggplot2 to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.

r-baymds 2.1
Propagated dependencies: r-shinythemes@1.2.0 r-shiny@1.11.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-progress@1.2.3 r-ggpubr@0.6.2 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=bayMDS
Licenses: GPL 2+
Synopsis: Bayesian Multidimensional Scaling and Choice of Dimension
Description:

Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001) <doi:10.1198/016214501753208690>.

r-bayesssm 0.7.1
Propagated dependencies: r-rcpp@1.1.0 r-mass@7.3-65 r-future-apply@1.20.0 r-future@1.68.0 r-dplyr@1.1.4 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/BjarkeHautop/bayesSSM
Licenses: Expat
Synopsis: Bayesian Methods for State Space Models
Description:

This package implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

r-bwd 0.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bwd
Licenses: GPL 2
Synopsis: Backward Procedure for Change-Point Detection
Description:

This package implements a backward procedure for single and multiple change point detection proposed by Shin et al. <arXiv:1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.

r-bbreg 2.0.2
Propagated dependencies: r-statmod@1.5.1 r-pbapply@1.7-4 r-formula@1.2-5 r-expint@0.1-9
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bbreg
Licenses: GPL 2
Synopsis: Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data
Description:

This package provides functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.

r-bclustlong 0.1.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mcclust@1.0.1 r-mass@7.3-65 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=BClustLonG
Licenses: GPL 2
Synopsis: Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data
Description:

Many clustering methods have been proposed, but most of them cannot work for longitudinal gene expression data. BClustLonG is a package that allows us to perform clustering analysis for longitudinal gene expression data. It adopts a linear-mixed effects framework to model the trajectory of genes over time, while clustering is jointly conducted based on the regression coefficients obtained from all genes. To account for the correlations among genes and alleviate the high dimensionality challenges, factor analysis models are adopted for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. This package allows users to specify which variables to use for clustering (intercepts or slopes or both) and whether a factor analysis model is desired. More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.

r-bradleyterry2 1.1.3
Propagated dependencies: r-qvcalc@1.0.4 r-lme4@1.1-37 r-gtools@3.9.5 r-brglm@0.7.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/hturner/BradleyTerry2
Licenses: GPL 2+
Synopsis: Bradley-Terry Models
Description:

Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.

r-bayesianou 0.1.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/isadorenabi/bayesianOU
Licenses: Expat
Synopsis: Bayesian Nonlinear Ornstein-Uhlenbeck Models with Stochastic Volatility
Description:

Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift, stochastic volatility, and Student-t innovations. The package implements hierarchical priors for sector-specific parameters and supports parallel MCMC sampling via Stan'. Model comparison is performed using Pareto Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation following Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. Prior specifications follow recommendations from Gelman (2006) <doi:10.1214/06-BA117A> for scale parameters.

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