_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

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-shinyradiomatrix 0.2.1
Propagated dependencies: r-shiny@1.11.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=shinyRadioMatrix
Licenses: GPL 3
Build system: r
Synopsis: Create a Matrix with Radio Buttons
Description:

An input controller for R Shiny: a matrix with radio buttons, where only one option per row can be selected.

r-spnn 1.3.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spnn
Licenses: GPL 2+
Build system: r
Synopsis: Scale Invariant Probabilistic Neural Networks
Description:

Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.

r-smoothedlasso 1.6
Propagated dependencies: r-rdpack@2.6.4 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=smoothedLasso
Licenses: GPL 2+
Build system: r
Synopsis: Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing
Description:

We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided.

r-sisireg 1.2.1
Propagated dependencies: r-zoo@1.8-14 r-reticulate@1.44.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sisireg
Licenses: GPL 2+
Build system: r
Synopsis: Sign-Simplicity-Regression-Solver
Description:

Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").

r-see 0.13.0
Propagated dependencies: r-performance@0.15.2 r-patchwork@1.3.2 r-parameters@0.28.3 r-modelbased@0.13.1 r-insight@1.4.3 r-ggplot2@4.0.1 r-effectsize@1.0.1 r-datawizard@1.3.0 r-correlation@0.8.8 r-bayestestr@0.17.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://easystats.github.io/see/
Licenses: Expat
Build system: r
Synopsis: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
Description:

This package provides plotting utilities supporting packages in the easystats ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for ggplot2'. Color scales are based on <https://materialui.co/>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.

r-stroke 25.9.2
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rankinplot@1.1.0 r-mass@7.3-65 r-lubridate@1.9.4 r-gtsummary@2.5.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-calendar@0.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://agdamsbo.github.io/stRoke/
Licenses: GPL 3
Build system: r
Synopsis: Clinical Stroke Research
Description:

This package provides a collection of tools for clinical trial data management and analysis in research and teaching. The package is mainly collected for personal use, but any use beyond that is encouraged. This package has migrated functions from agdamsbo/daDoctoR', and new functions has been added. Version follows months and year. See NEWS/Changelog for release notes. This package includes sampled data from the TALOS trial (Kraglund et al (2018) <doi:10.1161/STROKEAHA.117.020067>). The win_prob() function is based on work by Zou et al (2022) <doi:10.1161/STROKEAHA.121.037744>. The age_calc() function is based on work by Becker (2020) <doi:10.18637/jss.v093.i02>.

r-scagnostics 0.2-6
Propagated dependencies: r-rjava@1.0-11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://www.rforge.net/scagnostics/
Licenses: FreeBSD
Build system: r
Synopsis: Compute scagnostics - scatterplot diagnostics
Description:

Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot. They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.

r-sparrafairness 0.1.0.0
Propagated dependencies: r-scales@1.4.0 r-ranger@0.17.0 r-patchwork@1.3.2 r-mvtnorm@1.3-3 r-matrixstats@1.5.0 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-cvauc@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SPARRAfairness
Licenses: GPL 3+
Build system: r
Synopsis: Analysis of Differential Behaviour of SPARRA Score Across Demographic Groups
Description:

The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset. Our manuscript can be found at: <doi:10.1371/journal.pdig.0000675>.

r-sanvi 0.1.1
Propagated dependencies: r-scales@1.4.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-matrixstats@1.5.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/fradenti/SANvi
Licenses: Expat
Build system: r
Synopsis: Fitting Shared Atoms Nested Models via Variational Bayes
Description:

An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>.

r-somnmr 0.3.0
Propagated dependencies: r-rlang@1.1.6 r-quadprog@1.5-8 r-pracma@2.4.6 r-minpack-lm@1.2-4 r-intervalsurgeon@1.3 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/LuisCol8/SOMnmR/
Licenses: Expat
Build system: r
Synopsis: Analysis of Soil Organic Matter using Nuclear Magnetic Resonance
Description:

Integrates the 13C nuclear magnetic resonance spectra using different integration ranges. Output depends on the method chosen. For the Molecular Mixing Model, a measurement of the fitting quality is given by its R-factor. For more details see: <doi:10.5281/zenodo.10137768>.

r-sparkbq 0.1.1
Propagated dependencies: r-sparklyr@1.9.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://www.mirai-solutions.com
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Google 'BigQuery' Support for 'sparklyr'
Description:

This package provides a sparklyr extension package providing an integration with Google BigQuery'. It supports direct import/export where records are directly streamed from/to BigQuery'. In addition, data may be imported/exported via intermediate data extracts on Google Cloud Storage'.

r-scraep 1.2
Propagated dependencies: r-xml2@1.5.0 r-xml@3.99-0.20 r-rvest@1.0.5 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scraEP
Licenses: GPL 3+
Build system: r
Synopsis: Scrape the Web with Extra Power
Description:

This package provides tools for scraping information from webpages and other XML contents, using XPath or CSS selectors.

r-sdprior 1.0-0
Propagated dependencies: r-pscl@1.5.9 r-mvtnorm@1.3-3 r-mgcv@1.9-4 r-mass@7.3-65 r-gb2@2.1.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sdPrior
Licenses: GPL 2
Build system: r
Synopsis: Scale-Dependent Hyperpriors in Structured Additive Distributional Regression
Description:

Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.

r-starstileserver 0.1.1
Propagated dependencies: r-units@1.0-0 r-stars@0.6-8 r-sf@1.0-23 r-rlang@1.1.6 r-r6@2.6.1 r-png@0.1-8 r-plumber@1.3.0 r-leaflet@2.2.3 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://bartk.gitlab.io/starsTileServer
Licenses: GPL 3
Build system: r
Synopsis: Dynamic Tile Server for R
Description:

Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.

r-sgmrfmix 0.3.0
Propagated dependencies: r-zoo@1.8-14 r-tidyr@1.3.1 r-mvtnorm@1.3-3 r-glasso@1.11 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sGMRFmix
Licenses: Expat
Build system: r
Synopsis: Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection
Description:

An implementation of sparse Gaussian Markov random field mixtures presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>. It provides a novel anomaly detection method for multivariate noisy sensor data. It can automatically handle multiple operational modes. And it can also compute variable-wise anomaly scores.

r-scda 0.0.2
Propagated dependencies: r-spdep@1.4-1 r-spatialreg@1.4-2 r-sp@2.2-0 r-sf@1.0-23 r-rlang@1.1.6 r-performance@0.15.2 r-nbclust@3.0.1 r-ggspatial@1.1.10 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SCDA
Licenses: GPL 2+
Build system: r
Synopsis: Spatially-Clustered Data Analysis
Description:

This package contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>. Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.

r-suggests 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/owenjonesuob/suggests
Licenses: Expat
Build system: r
Synopsis: Declare when Suggested Packages are Needed
Description:

By adding dependencies to the "Suggests" field of a package's DESCRIPTION file, and then declaring that they are needed within any dependent functionality, it is often possible to significantly reduce the number of "hard" dependencies required by a package. This package provides a minimal way to declare when a suggested package is needed.

r-sinew 0.4.0
Propagated dependencies: r-yaml@2.3.10 r-stringi@1.8.7 r-sos@2.1-8 r-rstudioapi@0.17.1 r-rematch2@2.1.2 r-crayon@1.5.3 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/yonicd/sinew
Licenses: Expat
Build system: r
Synopsis: Package Development Documentation and Namespace Management
Description:

Manage package documentation and namespaces from the command line. Programmatically attach namespaces in R and Rmd script, populates Roxygen2 skeletons with information scraped from within functions and populate the Imports field of the DESCRIPTION file.

r-svmmaj 0.2.9.4
Propagated dependencies: r-scales@1.4.0 r-reshape2@1.4.5 r-kernlab@0.9-33 r-gridextra@2.3 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SVMMaj
Licenses: GPL 2
Build system: r
Synopsis: Implementation of the SVM-Maj Algorithm
Description:

This package implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.

r-slide 1.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SLIDE
Licenses: GPL 3
Build system: r
Synopsis: Single Cell Linkage by Distance Estimation is SLIDE
Description:

This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.

r-splitwise 1.0.2
Propagated dependencies: r-rpart@4.1.24
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SplitWise
Licenses: GPL 3+
Build system: r
Synopsis: Hybrid Stepwise Regression with Single-Split Dummy Encoding
Description:

This package implements SplitWise', a hybrid regression approach that transforms numeric variables into either single-split (0/1) dummy variables or retains them as continuous predictors. The transformation is followed by stepwise selection to identify the most relevant variables. The default iterative mode adaptively explores partial synergies among variables to enhance model performance, while an alternative univariate mode applies simpler transformations independently to each predictor. For details, see Kurbucz et al. (2025) <doi:10.48550/arXiv.2505.15423>.

r-spedm 1.11
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-sdsfun@0.8.1 r-rcppthread@2.2.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://stscl.github.io/spEDM/
Licenses: GPL 3
Build system: r
Synopsis: Spatial Empirical Dynamic Modeling
Description:

Inferring causation from spatial cross-sectional data through empirical dynamic modeling (EDM), with methodological extensions including geographical convergent cross mapping from Gao et al. (2023) <doi:10.1038/s41467-023-41619-6>, as well as the spatial causality test following the approach of Herrera et al. (2016) <doi:10.1111/pirs.12144>, together with geographical pattern causality proposed in Zhang et al. (2025) <doi:10.1080/13658816.2025.2581207>.

r-stlnpp 0.5.0
Propagated dependencies: r-spatstat-univar@3.1-5 r-spatstat-random@3.4-3 r-spatstat-linnet@3.3-2 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-spatstat@3.4-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=stlnpp
Licenses: GPL 2+
Build system: r
Synopsis: Spatio-Temporal Analysis of Point Patterns on Linear Networks
Description:

Statistical analysis of spatio-temporal point processes on linear networks. This packages provides tools to visualise and analyse spatio-temporal point patterns on linear networks using first, second, and higher-order summary statistics.

r-sorptionanalysis 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SorptionAnalysis
Licenses: GPL 2
Build system: r
Synopsis: Static Adsorption Experiment Plotting and Analysis
Description:

This package provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.

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