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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-bayesianlaterality 0.1.2
Propagated dependencies: r-tmvtnorm@1.7 r-tidyr@1.3.1 r-rlang@1.1.6 r-rdpack@2.6.4 r-purrr@1.2.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/LCBC-UiO/BayesianLaterality
Licenses: GPL 3
Synopsis: Predict Brain Asymmetry Based on Handedness and Dichotic Listening
Description:

Functional differences between the cerebral hemispheres are a fundamental characteristic of the human brain. Researchers interested in studying these differences often infer underlying hemispheric dominance for a certain function (e.g., language) from laterality indices calculated from observed performance or brain activation measures . However, any inference from observed measures to latent (unobserved) classes has to consider the prior probability of class membership in the population. The provided functions implement a Bayesian model for predicting hemispheric dominance from observed laterality indices (Sorensen and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020, <doi:10.1080/1357650X.2020.1769124>).

r-bestie 0.1.5
Propagated dependencies: r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-bidag@2.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=Bestie
Licenses: GPL 3
Synopsis: Bayesian Estimation of Intervention Effects
Description:

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.

r-blsr 0.5.0
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/groditi/blsR
Licenses: Expat
Synopsis: Make Requests from the Bureau of Labor Statistics API
Description:

This package implements v2 of the B.L.S. API for requests of survey information and time series data through 3-tiered API that allows users to interact with the raw API directly, create queries through a functional interface, and re-shape the data structures returned to fit common uses. The API definition is located at: <https://www.bls.gov/developers/api_signature_v2.htm>.

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
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-bigplsr 0.7.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-bigmemory@4.6.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://fbertran.github.io/bigPLSR/
Licenses: GPL 3
Synopsis: Partial Least Squares Regression Models with Big Matrices
Description:

Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls and widekernelpls') with parity to pls'. Optimized for bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and <https://hal.science/hal-05352061> highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) <doi:10.1002/(SICI)1099-128X(199701)11:1%3C73::AID-CEM435%3E3.0.CO;2-%23>, Rosipal & Trejo (2001) <https://www.jmlr.org/papers/v2/rosipal01a.html>, Tenenhaus, Viennet, and Saporta (2007) <doi:10.1016/j.csda.2007.01.004>, Rosipal (2004) <doi:10.1007/978-3-540-45167-9_17>, Rosipal (2019) <https://ieeexplore.ieee.org/document/8616346>, Song, Wang, and Bai (2024) <doi:10.1016/j.chemolab.2024.105238>. Includes kernel logistic PLS with C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates.

r-bulkreadr 1.2.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-sjlabelled@1.2.0 r-rlang@1.1.6 r-readxl@1.4.5 r-readr@2.1.6 r-purrr@1.2.0 r-openxlsx@4.2.8.1 r-magrittr@2.0.4 r-lubridate@1.9.4 r-labelled@2.16.0 r-inspectdf@0.0.12.1 r-haven@2.5.5 r-googlesheets4@1.1.2 r-fs@1.6.6 r-dplyr@1.1.4 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/gbganalyst/bulkreadr
Licenses: Expat
Synopsis: The Ultimate Tool for Reading Data in Bulk
Description:

Designed to simplify and streamline the process of reading and processing large volumes of data in R, this package offers a collection of functions tailored for bulk data operations. It enables users to efficiently read multiple sheets from Microsoft Excel and Google Sheets workbooks, as well as various CSV files from a directory. The data is returned as organized data frames, facilitating further analysis and manipulation. Ideal for handling extensive data sets or batch processing tasks, bulkreadr empowers users to manage data in bulk effortlessly, saving time and effort in data preparation workflows. Additionally, the package seamlessly works with labelled data from SPSS and Stata.

r-bootimpute 1.3.0
Propagated dependencies: r-smcfcs@2.0.1 r-mice@3.18.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bootImpute
Licenses: GPL 3
Synopsis: Bootstrap Inference for Multiple Imputation
Description:

Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.

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+
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-biodry 0.9.1
Propagated dependencies: r-nlme@3.1-168 r-ecodist@2.1.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BIOdry
Licenses: GPL 3
Synopsis: Multilevel Modeling of Dendroclimatical Fluctuations
Description:

Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity. Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.

r-bmass 1.0.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/mturchin20/bmass
Licenses: GPL 3+
Synopsis: Bayesian Multivariate Analysis of Summary Statistics
Description:

Multivariate tool for analyzing genome-wide association study results in the form of univariate summary statistics. The goal of bmass is to comprehensively test all possible multivariate models given the phenotypes and datasets provided. Multivariate models are determined by assigning each phenotype to being either Unassociated (U), Directly associated (D) or Indirectly associated (I) with the genetic variant of interest. Test results for each model are presented in the form of Bayes factors, thereby allowing direct comparisons between models. The underlying framework implemented here is based on the modeling developed in "A Unified Framework for Association Analysis with Multiple Related Phenotypes", M. Stephens (2013) <doi:10.1371/journal.pone.0065245>.

r-bayesrel 0.7.8
Propagated dependencies: r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-psych@2.5.6 r-mass@7.3-65 r-lavaan@0.6-20 r-laplacesdemon@16.1.6 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/juliuspfadt/Bayesrel
Licenses: GPL 3
Synopsis: Bayesian Reliability Estimation
Description:

Functionality for reliability estimates. For unidimensional tests: Coefficient alpha, Guttman's lambda-2/-4/-6, the Greatest lower bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. For multidimensional tests: omega_t (total) and omega_h (hierarchical). The results include confidence and credible intervals, the probability of a coefficient being larger than a cutoff, and a check for the factor models, necessary for the omega coefficients. The method for the Bayesian unidimensional estimates, except for omega_u, is sampling from the posterior inverse Wishart for the covariance matrix based measures (see Murphy', 2007, <https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. The Bayesian omegas (u, t, and h) are obtained by Gibbs sampling from the conditional posterior distributions of (1) the single factor model, (2) the second-order factor model, (3) the bi-factor model, (4) the correlated factor model ('Lee', 2007, <doi:10.1002/9780470024737>).

r-bayescombo 1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/stanlazic/BayesCombo
Licenses: GPL 3
Synopsis: Bayesian Evidence Combination
Description:

Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.

r-bst 0.3-24
Propagated dependencies: r-rpart@4.1.24 r-gbm@2.2.2 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://cran.r-project.org/package=bst
Licenses: GPL 2+
Synopsis: Gradient Boosting
Description:

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

r-bootstepaic 1.3-0
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+
Synopsis: Bootstrap stepAIC
Description:

Model selection by bootstrapping the stepAIC() procedure.

r-bpmnvisualizationr 0.5.0
Propagated dependencies: r-xml2@1.5.0 r-rlang@1.1.6 r-htmlwidgets@1.6.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://process-analytics.github.io/bpmn-visualization-R/
Licenses: FSDG-compatible
Synopsis: Visualize Process Execution Data on 'BPMN' Diagrams
Description:

To visualize the execution data of the processes on BPMN (Business Process Model and Notation) diagrams, using overlays, style customization and interactions, with the bpmn-visualization TypeScript library.

r-binsreg 1.1
Propagated dependencies: r-sandwich@3.1-1 r-quantreg@6.1 r-matrixstats@1.5.0 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=binsreg
Licenses: GPL 2
Synopsis: Binscatter Estimation and Inference
Description:

This package provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2024a) <doi:10.48550/arXiv.1902.09608>, Cattaneo, Crump, Farrell and Feng (2024b) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_NonlinearBinscatter.pdf> and Cattaneo, Crump, Farrell and Feng (2024c) <doi:10.48550/arXiv.1902.09615>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.

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
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-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+
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-bayfoxr 0.0.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/brews/bayfoxr/
Licenses: GPL 3+
Synopsis: Global Bayesian Foraminifera Core Top Calibration
Description:

This package provides a Bayesian, global planktic foraminifera core top calibration to modern sea-surface temperatures. Includes four calibration models, considering species-specific calibration parameters and seasonality.

r-boostingdea 0.1.0
Propagated dependencies: r-rglpk@0.6-5.1 r-mlmetrics@1.1.3 r-lpsolveapi@5.5.2.0-17.14 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/itsmeryguillen/boostingDEA
Licenses: AGPL 3+
Synopsis: Boosting Approach to Data Envelopment Analysis
Description:

Includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context using both standard models from DEA and Free Disposal Hull (FDH) and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package includes code for estimating several technical efficiency measures using different models such as the input and output-oriented radial measures, the input and output-oriented Russell measures, the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based Measure (SBM).

r-bartxviz 1.0.9
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@1.3.4.1 r-bart@2.9.9 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+
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-bionetdata 1.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bionetdata
Licenses: GPL 2+
Synopsis: Biological and Chemical Data Networks
Description:

Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.

r-bayesiandisaggregation 0.1.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-readxl@1.4.5 r-openxlsx@4.2.8.1 r-magrittr@2.0.4 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesianDisaggregation
Licenses: Expat
Synopsis: Bayesian Methods for Economic Data Disaggregation
Description:

This package implements a novel Bayesian disaggregation framework that combines Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) dimension reduction of prior weight matrices with deterministic Bayesian updating rules. The method provides Markov Chain Monte Carlo (MCMC) free posterior estimation with built-in diagnostic metrics. While based on established PCA (Jolliffe, 2002) <doi:10.1007/b98835> and Bayesian principles (Gelman et al., 2013) <doi:10.1201/b16018>, the specific integration for economic disaggregation represents an original methodological contribution.

r-brunnermunzel 2.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/toshi-ara/brunnermunzel
Licenses: GPL 2 GPL 3
Synopsis: (Permuted) Brunner-Munzel Test
Description:

This package provides the functions for Brunner-Munzel test and permuted Brunner-Munzel test, which enable to use formula, matrix, and table as argument. These functions are based on Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U> and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>, and are written with FORTRAN.

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