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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-bambi 2.3.6
Propagated dependencies: r-scales@1.4.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-qrng@0.0-11 r-numderiv@2016.8-1.1 r-mvtnorm@1.3-3 r-loo@2.8.0 r-lattice@0.22-7 r-label-switching@1.8 r-gtools@3.9.5 r-future-apply@1.20.0 r-coda@0.19-4.1 r-bridgesampling@1.2-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://doi.org/10.18637/jss.v099.i11
Licenses: GPL 3
Build system: r
Synopsis: Bivariate Angular Mixture Models
Description:

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.

r-bcmixed 0.1.5
Propagated dependencies: r-nlme@3.1-168 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=bcmixed
Licenses: GPL 2+
Build system: r
Synopsis: Mixed Effect Model with the Box-Cox Transformation
Description:

Inference on the marginal model of the mixed effect model with the Box-Cox transformation and on the model median differences between treatment groups for longitudinal randomized clinical trials. These statistical methods are proposed by Maruo et al. (2017) <doi:10.1002/sim.7279>.

r-binaryeppm 3.0
Propagated dependencies: r-numderiv@2016.8-1.1 r-lmtest@0.9-40 r-formula@1.2-5 r-expm@1.0-0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BinaryEPPM
Licenses: GPL 2
Build system: r
Synopsis: Mean and Scale-Factor Modeling of Under- And Over-Dispersed Binary Data
Description:

Under- and over-dispersed binary data are modeled using an extended Poisson process model (EPPM) appropriate for binary data. A feature of the model is that the under-dispersion relative to the binomial distribution only needs to be greater than zero, but the over-dispersion is restricted compared to other distributional models such as the beta and correlated binomials. Because of this, the examples focus on under-dispersed data and how, in combination with the beta or correlated distributions, flexible models can be fitted to data displaying both under- and over-dispersion. Using Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for the probability of success and scale-factor with various link functions for p, and log link for scale-factor, to fit a variety of models relevant to areas such as bioassay. Details of the EPPM are in Faddy and Smith (2012) <doi:10.1002/bimj.201100214> and Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.

r-bdesize 1.6
Propagated dependencies: r-ggplot2@4.0.1 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-bayeswatch 0.1.4
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-mass@7.3-65 r-hotelling@1.0-8 r-gridextra@2.3 r-ggplot2@4.0.1 r-ess@1.1.2.1 r-cholwishart@1.1.4 r-bh@1.87.0-1 r-bdgraph@2.74
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bayesWatch
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Change-Point Detection for Process Monitoring with Fault Detection
Description:

Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023) <doi:10.48550/arXiv.2310.02940>.

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
Build system: r
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-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
Build system: r
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-bttest 0.10.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Paul-Haimerl/BTtest
Licenses: GPL 3+
Build system: r
Synopsis: Estimate the Number of Factors in Large Nonstationary Datasets
Description:

Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.

r-bakerrr 0.2.0
Propagated dependencies: r-s7@0.2.1 r-purrr@1.2.0 r-mirai@2.5.2 r-glue@1.8.0 r-fs@1.6.6 r-config@0.3.2 r-cli@3.6.5 r-carrier@0.3.0.4 r-callr@3.7.6
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/anirbanshaw24/bakerrr
Licenses: Expat
Build system: r
Synopsis: Background-Parallel Jobs
Description:

Easily launch, track, and control functions as background-parallel jobs. Includes robust utilities for job status, error handling, resource monitoring, and result collection. Designed for scalable workflows in interactive and automated settings (local or remote). Integrates with multiple backends; supports flexible automation pipelines and live job tracking. For more information, see <https://anirbanshaw24.github.io/bakerrr/>.

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-batchmeans 1.0-4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=batchmeans
Licenses: GPL 2+
Build system: r
Synopsis: Consistent Batch Means Estimation of Monte Carlo Standard Errors
Description:

This package provides consistent batch means estimation of Monte Carlo standard errors.

r-babytimer 0.1.0
Propagated dependencies: r-stringr@1.6.0 r-snakecase@0.11.1 r-readr@2.1.6 r-lubridate@1.9.4 r-janitor@2.2.1 r-glue@1.8.0 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=babyTimeR
Licenses: Expat
Build system: r
Synopsis: Parse Output from 'BabyTime' Application
Description:

BabyTime is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. It handles malformed and incomplete data gracefully and is designed to parse one directory at a time.

r-barnard 1.8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/kerguler/Barnard
Licenses: GPL 2
Build system: r
Synopsis: Barnard's Unconditional Test
Description:

Barnard's unconditional test for 2x2 contingency tables.

r-beezdemand 0.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/brentkaplan/beezdemand
Licenses: GPL 2 FSDG-compatible
Build system: r
Synopsis: Behavioral Economic Easy Demand
Description:

Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential (Hursh & Silberberg, 2008, <doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data.

r-brand-yml 0.1.0
Propagated dependencies: r-yaml@2.3.10 r-rlang@1.1.6 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://posit-dev.github.io/brand-yml/pkg/r/
Licenses: Expat
Build system: r
Synopsis: Unified Branding with a Simple YAML File
Description:

Read and process brand.yml YAML files. brand.yml is a simple, portable YAML file that codifies your company's brand guidelines into a format that can be used by Quarto', Shiny and R tooling to create branded outputs. Maintain unified, branded theming for web applications to printed reports to dashboards and presentations with a consistent look and feel.

r-bivarian 1.0.3
Propagated dependencies: r-tidyr@1.3.1 r-table1@1.5.1 r-systemfonts@1.3.1 r-scales@1.4.0 r-rrtable@0.3.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-logistf@1.26.1 r-lifecycle@1.0.4 r-glue@1.8.0 r-ggprism@1.0.7 r-ggplot2@4.0.1 r-fastdummies@1.7.5 r-epitools@0.5-10.1 r-dplyr@1.1.4 r-desctools@0.99.60 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/AndresFloresG/BiVariAn
Licenses: GPL 3+
Build system: r
Synopsis: Bivariate Automatic Analysis
Description:

Simplify bivariate and regression analyses by automating result generation, including summary tables, statistical tests, and customizable graphs. It supports tests for continuous and dichotomous data, as well as stepwise regression for linear, logistic, and Firth penalized logistic models. While not a substitute for tailored analysis, BiVariAn accelerates workflows and is expanding features like multilingual interpretations of results.The methods for selecting significant statistical tests, as well as the predictor selection in prediction functions, can be referenced in the works of Marc Kery (2003) <doi:10.1890/0012-9623(2003)84[92:NORDIG]2.0.CO;2> and Rainer Puhr (2017) <doi:10.1002/sim.7273>.

r-bmt 0.1.3
Propagated dependencies: r-partitions@1.10-9 r-fitdistrplus@1.2-4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BMT
Licenses: GPL 2+
Build system: r
Synopsis: The BMT Distribution
Description:

Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) <doi:10.48550/arXiv.1709.05534>. Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included.

r-bfsmaps 1.99.4
Propagated dependencies: r-sf@1.0-23 r-httr@1.4.7 r-desctools@0.99.60
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/AndriSignorell/bfsMaps/
Licenses: GPL 2+
Build system: r
Synopsis: Plot Maps from Switzerland by Swiss Federal Statistical Office
Description:

At the Swiss Federal Statistical Office (SFSO), spatial maps of Switzerland are available free of charge as Cartographic bases for small-scale thematic mapping'. This package contains convenience functions to import ESRI (Environmental Systems Research Institute) shape files using the package sf and to plot them easily and quickly without having to worry too much about the technical details. It contains utilities to combine multiple areas to one single polygon and to find neighbours for single regions. For any point on a map, a special locator can be used to determine to which municipality, district or canton it belongs.

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-balli 0.2.0
Propagated dependencies: r-mass@7.3-65 r-limma@3.66.0 r-edger@4.8.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BALLI
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Expression RNA-Seq Data Analysis Based on Linear Mixed Model
Description:

Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.

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+
Build system: r
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-basemodels 1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Ying-Ju/basemodels
Licenses: Expat
Build system: r
Synopsis: Baseline Models for Classification and Regression
Description:

Providing equivalent functions for the dummy classifier and regressor used in Python scikit-learn library. Our goal is to allow R users to easily identify baseline performance for their classification and regression problems. Our baseline models use no predictors, and are useful in cases of class imbalance, multiclass classification, and when users want to quickly identify how much improvement their statistical and machine learning models are over several baseline models. We use a "better" default (proportional guessing) for the dummy classifier than the Python implementation ("prior", which is the most frequent class in the training set). The functions in the package can be used on their own, or introduce methods named dummy_regressor or dummy_classifier that can be used within the caret package pipeline.

r-bdribs 1.0.4
Propagated dependencies: r-rjags@4-17
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bdribs
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data
Description:

This package implements Bayesian inference to detect signal from blinded clinical trial when total number of adverse events of special concerns and total risk exposures from all patients are available in the study. For more details see the article by Mukhopadhyay et. al. (2018) titled Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data', in Pharmaceutical Statistics (to appear).

r-bbi 0.3.0
Propagated dependencies: r-vegan@2.7-2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/trtcrd/BBI
Licenses: AGPL 3 FSDG-compatible
Build system: r
Synopsis: Benthic Biotic Indices Calculation from Composition Data
Description:

Set of functions to calculate Benthic Biotic Indices from composition data, obtained whether from morphotaxonomic inventories or sequencing data. Based on reference ecological weights publicly available for a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) <doi:10.1016/S0025-326X(00)00061-8> NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio.

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