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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-brazilmet 0.4.0
Propagated dependencies: r-tibble@3.3.0 r-terra@1.8-86 r-stringr@1.6.0 r-stringi@1.8.7 r-sf@1.0-23 r-readxl@1.4.5 r-lubridate@1.9.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BrazilMet
Licenses: GPL 3
Build system: r
Synopsis: Download and Processing of Automatic Weather Stations (AWS) Data of INMET-Brazil
Description:

This package provides a collection of functions for downloading and processing automatic weather station (AWS) data from INMET (Brazilâ s National Institute of Meteorology), designed to support the estimation of reference evapotranspiration (ETo). The package facilitates streamlined access to meteorological data and aims to simplify analyses in agricultural and environmental contexts.

r-bucss 1.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BUCSS
Licenses: GPL 3+
Build system: r
Synopsis: Bias and Uncertainty Corrected Sample Size
Description:

Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).

r-blockcluster 4.5.5
Propagated dependencies: r-rtkore@1.6.13 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://gitlab.inria.fr/iovleff/blockcluster
Licenses: GPL 3+
Build system: r
Synopsis: Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets
Description:

Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets. The blockcluster package provides a bridge between the C++ core library build on top of the STK++ library, and the R statistical computing environment. This package allows to co-cluster binary <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>, continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets <doi:10.1007/s11222-014-9472-2>. It also provides utility functions to visualize the results. This package may be useful for various applications in fields of Data mining, Information retrieval, Biology, computer vision and many more. More information about the project and comprehensive tutorial can be found on the link mentioned in URL.

r-basifor 0.4.1
Propagated dependencies: r-rodbc@1.3-26.1 r-measurements@1.5.1 r-httr@1.4.7 r-hmisc@5.2-4 r-foreign@0.8-90 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=basifoR
Licenses: GPL 3
Build system: r
Synopsis: Retrieval and Processing of the Spanish National Forest Inventory
Description:

Data sets of the Spanish National Forest Inventory <https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible.html> are processed to compute tree metrics and statistics. Function metrics2Vol() controls most of the routines.

r-bvls 1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bvls
Licenses: GPL 2+
Build system: r
Synopsis: The Stark-Parker algorithm for bounded-variable least squares
Description:

An R interface to the Stark-Parker implementation of an algorithm for bounded-variable least squares.

r-baldur 0.0.4
Propagated dependencies: r-viridislite@0.4.2 r-tidyr@1.3.1 r-tibble@3.3.0 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-rdpack@2.6.4 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-purrr@1.2.0 r-multidplyr@0.1.4 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/PhilipBerg/baldur
Licenses: Expat
Build system: r
Synopsis: Bayesian Hierarchical Modeling for Label-Free Proteomics
Description:

Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (<doi:10.1016/j.mcpro.2023.100658>).

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+
Build system: r
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-bennu 0.3.2
Propagated dependencies: r-tidyr@1.3.1 r-tidybayes@3.0.7 r-stanheaders@2.32.10 r-scales@1.4.0 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-magrittr@2.0.4 r-lifecycle@1.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-generics@0.1.4 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://sempwn.github.io/bennu/
Licenses: Expat
Build system: r
Synopsis: Bayesian Estimation of Naloxone Kit Number Under-Reporting
Description:

Bayesian model and associated tools for generating estimates of total naloxone kit numbers distributed and used from naloxone kit orders data. Provides functions for generating simulated data of naloxone kit use and functions for generating samples from the posterior.

r-basefun 1.2-5
Propagated dependencies: r-variables@1.1-2 r-polynom@1.4-1 r-orthopolynom@1.0-6.1 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: http://ctm.R-forge.R-project.org
Licenses: GPL 2
Build system: r
Synopsis: Infrastructure for Computing with Basis Functions
Description:

Some very simple infrastructure for basis functions.

r-bdwreg 1.3.0
Propagated dependencies: r-mass@7.3-65 r-foreach@1.5.2 r-dwreg@3.0 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=BDWreg
Licenses: LGPL 2.0+
Build system: r
Synopsis: Bayesian Inference for Discrete Weibull Regression
Description:

This package provides a Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.

r-bingsd 1.1
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BinGSD
Licenses: GPL 3
Build system: r
Synopsis: Calculate Boundaries and Conditional Power for Single Arm Group Sequential Test with Binary Endpoint
Description:

Consider an at-most-K-stage group sequential design with only an upper bound for the last analysis and non-binding lower bounds.With binary endpoint, two kinds of test can be applied, asymptotic test based on normal distribution and exact test based on binomial distribution. This package supports the computation of boundaries and conditional power for single-arm group sequential test with binary endpoint, via either asymptotic or exact test. The package also provides functions to obtain boundary crossing probabilities given the design.

r-bimets 4.1.2
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/andrea-luciani/bimets
Licenses: GPL 3
Build system: r
Synopsis: Time Series and Econometric Modeling
Description:

Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, rational expectations, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control, by A. Luciani (2022) <doi:10.13140/RG.2.2.31160.83202>.

r-bchron 4.7.8
Propagated dependencies: r-stringr@1.6.0 r-scales@1.4.0 r-purrr@1.2.0 r-mclust@6.1.2 r-mass@7.3-65 r-magrittr@2.0.4 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-dplyr@1.1.4 r-coda@0.19-4.1 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://andrewcparnell.github.io/Bchron/
Licenses: GPL 2+
Build system: r
Synopsis: Age-Depth Radiocarbon Modelling
Description:

Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the Oxcal function SUM(); currently unpublished), and reverse calibration of dates from calibrated into 14C years (also unpublished).

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-bnrich 0.1.1
Propagated dependencies: r-graph@1.88.0 r-glmnet@4.1-10 r-corpcor@1.6.10 r-bnlearn@5.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Samaneh-Bioinformatics/BNrich
Licenses: GPL 2+
Build system: r
Synopsis: Pathway Enrichment Analysis Based on Bayesian Network
Description:

Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisherâ s Exact Test on significant parameters.

r-bdlp 0.9-2
Propagated dependencies: r-stringdist@0.9.15 r-rsqlite@2.4.4 r-rgl@1.3.31 r-multiord@2.4.4 r-mass@7.3-65 r-genord@2.0.0 r-dbi@1.2.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bdlp
Licenses: GPL 2
Build system: r
Synopsis: Transparent and Reproducible Artificial Data Generation
Description:

The main function generateDataset() processes a user-supplied .R file that contains metadata parameters in order to generate actual data. The metadata parameters have to be structured in the form of metadata objects, the format of which is outlined in the package vignette. This approach allows to generate artificial data in a transparent and reproducible manner.

r-bayesx 0.3-3
Propagated dependencies: r-sp@2.2-0 r-shapefiles@0.7.2 r-sf@1.0-23 r-interp@1.1-6 r-colorspace@2.1-2 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=BayesX
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: R Utilities Accompanying the Software Package BayesX
Description:

This package provides functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.

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
Build system: r
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-bingroup 2.2-3
Propagated dependencies: r-rdpack@2.6.4 r-partitions@1.10-9
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=binGroup
Licenses: GPL 3+
Build system: r
Synopsis: Evaluation and Experimental Design for Binomial Group Testing
Description:

This package provides methods for estimation and hypothesis testing of proportions in group testing designs: methods for estimating a proportion in a single population (assuming sensitivity and specificity equal to 1 in designs with equal group sizes), as well as hypothesis tests and functions for experimental design for this situation. For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations.

r-bkmr 0.2.2
Propagated dependencies: r-truncnorm@1.0-9 r-tmvtnorm@1.7 r-tidyr@1.3.1 r-tibble@3.3.0 r-nlme@3.1-168 r-mass@7.3-65 r-magrittr@2.0.4 r-fields@17.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jenfb/bkmr
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Kernel Machine Regression
Description:

Implementation of a statistical approach for estimating the joint health effects of multiple concurrent exposures, as described in Bobb et al (2015) <doi:10.1093/biostatistics/kxu058>.

r-bootgof 0.1.1
Propagated dependencies: r-r6@2.6.1 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/MarselScheer/bootGOF
Licenses: GPL 3
Build system: r
Synopsis: Bootstrap Based Goodness-of-Fit Tests
Description:

Bootstrap based goodness-of-fit tests. It allows to perform rigorous statistical tests to check if a chosen model family is correct based on the marked empirical process. The implemented algorithms are described in (Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>) and can be applied to generalized linear models without any further implementation effort. As far as certain linearity conditions are fulfilled the resampling scheme are also applicable beyond generalized linear models. This is reflected in the software architecture which allows to reuse the resampling scheme by implementing only certain interfaces for models that are not supported natively by the package.

r-buildsys 1.1.2
Propagated dependencies: r-digest@0.6.39
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/pjumppanen/BuildSys
Licenses: GPL 2
Build system: r
Synopsis: System for Building and Debugging C/C++ Dynamic Libraries
Description:

This package provides a build system based on GNU make that creates and maintains (simply) make files in an R session and provides GUI debugging support through Microsoft Visual Code'.

r-boptbd 1.0.7
Propagated dependencies: r-matrix@1.7-4 r-mass@7.3-65 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=Boptbd
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Optimal Block Designs
Description:

Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> and Gemechu, Debusho and Haines (2025) <doi:10.5539/ijsp.v14n1p50> where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.

r-bidsr 0.1.1
Propagated dependencies: r-uuid@1.2-1 r-s7@0.2.1 r-nanotime@0.3.12 r-jsonlite@2.0.0 r-fs@1.6.6 r-fastmap@1.2.0 r-data-table@1.17.8 r-checkmate@2.3.3
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.

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