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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-rebayes 2.60
Propagated dependencies: r-matrix@1.7-4 r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://www.r-project.org
Licenses: GPL 2+
Build system: r
Synopsis: Empirical Bayes Estimation and Inference
Description:

Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.

r-rnomni 1.0.1.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=RNOmni
Licenses: GPL 3
Build system: r
Synopsis: Rank Normal Transformation Omnibus Test
Description:

Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests outperform standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" <doi:10.1111/biom.13214>.

r-rquantlib 0.4.26
Propagated dependencies: r-zoo@1.8-14 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://github.com/eddelbuettel/rquantlib
Licenses: GPL 2+
Build system: r
Synopsis: R Interface to the 'QuantLib' Library
Description:

The RQuantLib package makes parts of QuantLib accessible from R The QuantLib project aims to provide a comprehensive software framework for quantitative finance. The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets.

r-ropendota 0.1.2
Propagated dependencies: r-rcurl@1.98-1.17 r-jsonlite@2.0.0
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://github.com/rosdyana/ROpenDota
Licenses: Expat
Build system: r
Synopsis: Access OpenDota Services in R
Description:

This package provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.

r-rramas 0.1-8
Propagated dependencies: r-diagram@1.6.5
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=Rramas
Licenses: GPL 2+
Build system: r
Synopsis: Matrix Population Models
Description:

Analyzes and predicts from matrix population models (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.

r-rgap 0.1.1
Propagated dependencies: r-zoo@1.8-14 r-openxlsx@4.2.8.1 r-kfas@1.6.0 r-gridextra@2.3 r-ggplot2@4.0.1 r-dlm@1.1-6.1
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=RGAP
Licenses: GPL 3
Build system: r
Synopsis: Production Function Output Gap Estimation
Description:

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. RGAP facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. RGAP features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the AMECO <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.

r-rabr 0.1.1
Propagated dependencies: r-survival@3.8-3 r-multxpert@0.1.1 r-multcomp@1.4-29 r-ggplot2@4.0.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-data-table@1.17.8 r-cubature@2.1.4-1 r-asd@2.2
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://github.com/tian-yu-zhan/RABR
Licenses: Expat
Build system: r
Synopsis: Simulations for Response Adaptive Block Randomization Design
Description:

Conduct simulations of the Response Adaptive Block Randomization (RABR) design to evaluate its type I error rate, power and operating characteristics for binary and continuous endpoints. For more details of the proposed method, please refer to Zhan et al. (2021) <doi:10.1002/sim.9104>.

r-remindr 0.0.1
Propagated dependencies: r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=remindR
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Insert and Extract "Reminders" from Function Comments
Description:

Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.

r-rcmdrplugin-bws3 0.3-1
Propagated dependencies: r-survival@3.8-3 r-support-ces@0.7-0 r-support-bws3@0.2-1 r-rcmdr@2.9-5
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://sites.google.com/view/r4sp/rcmdrplugin
Licenses: GPL 2+
Build system: r
Synopsis: R Commander Plug-in for Case 3 Best-Worst Scaling
Description:

Adds menu items for case 3 (multi-profile) best-worst scaling (BWS3) to the R Commander. BWS3 is a question-based survey method that designs various combinations of attribute levels (profiles), asks respondents to select the best and worst profiles in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on BWS3, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.

r-rcppmecab 0.0.1.2
Propagated dependencies: r-rcppparallel@5.1.11-1 r-rcpp@1.1.0 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=RcppMeCab
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: 'rcpp' Wrapper for 'mecab' Library
Description:

R package based on Rcpp for MeCab': Yet Another Part-of-Speech and Morphological Analyzer. The purpose of this package is providing a seamless developing and analyzing environment for CJK texts. This package utilizes parallel programming for providing highly efficient text preprocessing posParallel() function. For installation, please refer to README.md file.

r-riskregression 2026.02.13
Propagated dependencies: r-timereg@2.0.7 r-survival@3.8-3 r-rms@8.1-0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ranger@0.17.0 r-publish@2025.07.24 r-prodlim@2025.04.28 r-plotrix@3.8-13 r-mvtnorm@1.3-3 r-mets@1.3.9 r-lava@1.8.2 r-lattice@0.22-7 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-data-table@1.17.8 r-cmprsk@2.2-12
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://github.com/tagteam/riskRegression
Licenses: GPL 2+
Build system: r
Synopsis: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks
Description:

Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.

r-roxigraph 0.1.1
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://github.com/cboettig/roxigraph
Licenses: Expat
Build system: r
Synopsis: 'RDF' and 'SPARQL' for R using 'Oxigraph'
Description:

This package provides RDF storage and SPARQL 1.1 query capabilities by wrapping the Oxigraph graph database library <https://github.com/oxigraph/oxigraph>. Supports in-memory and persistent ('RocksDB') storage, multiple RDF serialization formats ('Turtle', N-Triples', RDF-XML', N-Quads', TriG'), and full SPARQL 1.1 Query and Update support. Built using the extendr framework for Rust'-R bindings.

r-rdimtools 1.1.3
Propagated dependencies: r-rspectra@0.16-2 r-rdpack@2.6.4 r-rcppdist@0.1.1.1 r-rcppde@0.1.8 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rann@2.6.2 r-mclustcomp@0.3.5 r-mass@7.3-65 r-maotai@0.3.0 r-cvxr@1.0-15 r-admm@0.3.4
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://www.kisungyou.com/Rdimtools/
Licenses: Expat
Build system: r
Synopsis: Dimension Reduction and Estimation Methods
Description:

We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.

r-robustsfa 0.2.0
Propagated dependencies: r-truncnorm@1.0-9 r-rcpp@1.1.0 r-frontier@1.1-8 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=robustSFA
Licenses: GPL 3
Build system: r
Synopsis: Robust Estimation of Stochastic Frontier Models with MDPDE
Description:

This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. Additionally, it includes a function to recommend the optimal tuning parameter, alpha, which controls the robustness of the MDPDE. The methods implemented in this package are based on Song et al. (2017) <doi:10.1016/j.csda.2016.08.005>.

r-robscale 0.1.1
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://github.com/davdittrich/robscale
Licenses: Expat
Build system: r
Synopsis: Fast Robust Estimation in Very Small Samples
Description:

High-performance C++ implementation (via Rcpp') of the robust location and scale M-estimators described in Rousseeuw & Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for very small samples. Provides numerically identical results to the revss package with significantly improved performance through sorting networks and compiled iteration loops.

r-rnetcarto 0.2.6
Dependencies: gsl@2.8
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=rnetcarto
Licenses: GPL 2+
Build system: r
Synopsis: Fast Network Modularity and Roles Computation by Simulated Annealing (Rgraph C Library Wrapper for R)
Description:

This package provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, <doi:10.1038/nature03288>).

r-regmed 2.1.5
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-lavaan@0.6-20 r-knitr@1.50 r-igraph@2.2.1 r-gtools@3.9.5 r-glasso@1.11
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=regmed
Licenses: GPL 2+
Build system: r
Synopsis: Regularized Mediation Analysis
Description:

Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).

r-rbtc 0.1-7
Propagated dependencies: r-rjson@0.2.23 r-openssl@2.3.4 r-httr@1.4.7 r-gmp@0.7-5
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=rbtc
Licenses: GPL 3
Build system: r
Synopsis: Bitcoin API
Description:

Implementation of the RPC-JSON API for Bitcoin and utility functions for address creation and content analysis of the blockchain.

r-rvif 3.2
Propagated dependencies: r-multicoll@2.0 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: http://colldetreat.r-forge.r-project.org/
Licenses: GPL 2+
Build system: r
Synopsis: Collinearity Detection using Redefined Variance Inflation Factor and Graphical Methods
Description:

The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., Garcà a C.B. and Garcà a J. (2018) <doi:10.1080/00949655.2018.1463376>, Salmerón, R., Rodrà guez, A. and Garcà a C. (2020) <doi:10.1007/s00180-019-00922-x>, Salmerón, R., Garcà a, C.B, Rodrà guez, A. and Garcà a, C. (2022) <doi:10.32614/RJ-2023-010>, Salmerón, R., Garcà a, C.B. and Garcà a, J. (2025) <doi:10.1007/s10614-024-10575-8> and Salmerón, R., Garcà a, C.B, Garcà a J. (2023, working paper) <doi:10.48550/arXiv.2005.02245>. You can also view the package vignette using browseVignettes("rvif")', the package website (<https://www.ugr.es/local/romansg/rvif/index.html>) using browseURL(system.file("docs/index.html", package = "rvif")) or version control on GitHub (<https://github.com/rnoremlas/rvif_package>).

r-rassta 1.0.6
Propagated dependencies: r-terra@1.8-86 r-stringi@1.8.7 r-stringdist@0.9.15 r-shiny@1.11.1 r-scales@1.4.0 r-rlang@1.1.6 r-plotly@4.11.0 r-kohonen@3.0.12 r-kernsmooth@2.23-26 r-histogram@0.0-25 r-ggplot2@4.0.1 r-ggally@2.4.0 r-foreach@1.5.2 r-dt@0.34.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://bafuentes.github.io/rassta/
Licenses: AGPL 3+
Build system: r
Synopsis: Raster-Based Spatial Stratification Algorithms
Description:

Algorithms for the spatial stratification of landscapes, sampling and modeling of spatially-varying phenomena. These algorithms offer a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena. The theoretical background of the algorithms and code examples are presented in Fuentes et al. (2022). <doi:10.32614/RJ-2022-036>.

r-rccola 1.0.2
Propagated dependencies: r-yaml@2.3.10 r-redcapapi@2.11.5 r-keyring@1.4.1 r-getpass@0.2-4
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://github.com/spgarbet/rccola
Licenses: GPL 3
Build system: r
Synopsis: Safely Manage API Keys and Load Data from a REDCap or Other Source
Description:

The handling of an API key (misnomer for password) for protected data can be difficult. This package provides secure convenience functions for entering / handling API keys and pulling data directly into memory. By default it will load from REDCap instances, but other sources are injectable via inversion of control.

r-rcmdrplugin-roc 1.0-19
Propagated dependencies: r-resourceselection@0.3-6 r-rcmdr@2.9-5 r-proc@1.19.0.1
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=RcmdrPlugin.ROC
Licenses: GPL 2+
Build system: r
Synopsis: Rcmdr Receiver Operator Characteristic Plug-in Package
Description:

Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC package. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.

r-rdrw 1.0.2
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/r.scm (guix-cran packages r)
Home page: https://cran.r-project.org/package=Rdrw
Licenses: GPL 2
Build system: r
Synopsis: Univariate and Multivariate Damped Random Walk Processes
Description:

We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) <arXiv:2005.08049>.

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