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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-spind 2.2.1
Propagated dependencies: r-waveslim@1.8.5 r-stringr@1.6.0 r-splancs@2.01-45 r-rlang@1.1.6 r-rje@1.12.1 r-rcolorbrewer@1.1-3 r-mass@7.3-65 r-lattice@0.22-7 r-ggplot2@4.0.1 r-geepack@1.3.13 r-gee@4.13-29
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/levisc8/spind
Licenses: GPL 3
Build system: r
Synopsis: Spatial Methods and Indices
Description:

This package provides functions for spatial methods based on generalized estimating equations (GEE) and wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR), conducting multi-model inference, and stepwise model selection. Further, contains functions for spatially corrected model accuracy measures.

r-statpermeco 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=StatPerMeCo
Licenses: GPL 2+
Build system: r
Synopsis: Statistical Performance Measures to Evaluate Covariance Matrix Estimates
Description:

Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>, Amendola et al. (2015) <doi:10.1002/for.2322> and Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.

r-sseparser 0.1.0
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/calderonsamuel/SSEparser
Licenses: Expat
Build system: r
Synopsis: Parse Server-Sent Events
Description:

Functionality to parse server-sent events with a high-level interface that can be extended for custom applications.

r-spcp 1.4.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-msm@1.8.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spCP
Licenses: GPL 2+
Build system: r
Synopsis: Spatially Varying Change Points
Description:

This package implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.

r-survsakk 1.3.3
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://sakk-statistics.github.io/survSAKK/
Licenses: GPL 2+
Build system: r
Synopsis: Create Publication Ready Kaplan-Meier Plots
Description:

Incorporate various statistics and layout customization options to enhance the efficiency and adaptability of the Kaplan-Meier plots.

r-spichanges 0.2.1
Propagated dependencies: r-zoo@1.8-14 r-spsutil@0.2.2.1 r-rlang@1.1.6 r-mumin@1.48.11 r-lubridate@1.9.4 r-gamlss-dist@6.1-1 r-gamlss@5.5-0 r-brglm2@1.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/gabrielblain/SPIChanges
Licenses: Expat
Build system: r
Synopsis: Improves the Interpretation of the Standardized Precipitation Index Under Changing Climate Conditions
Description:

Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) <doi:10.1002/joc.7550> to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring.

r-stochlab 1.1.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-pracma@2.4.6 r-msm@1.8.2 r-magrittr@2.0.4 r-logr@1.3.9 r-glue@1.8.0 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/HiDef-Aerial-Surveying/stochLAB
Licenses: GPL 3+
Build system: r
Synopsis: Stochastic Collision Risk Model
Description:

Collision Risk Models for avian fauna (seabird and migratory birds) at offshore wind farms. The base deterministic model is derived from Band (2012) <https://tethys.pnnl.gov/publications/using-collision-risk-model-assess-bird-collision-risks-offshore-wind-farms>. This was further expanded on by Masden (2015) <doi:10.7489/1659-1> and code used here is heavily derived from this work with input from Dr A. Cook at the British Trust for Ornithology. These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, many of the species included in the stochastic collision risk models can also be found in the North Atlantic in the United States and Canada, and could be applied there.

r-splitknockoff 2.1
Propagated dependencies: r-rspectra@0.16-2 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-mass@7.3-65 r-latex2exp@0.9.6 r-glmnet@4.1-10 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=SplitKnockoff
Licenses: Expat
Build system: r
Synopsis: Split Knockoffs for Structural Sparsity
Description:

Split Knockoff is a data adaptive variable selection framework for controlling the (directional) false discovery rate (FDR) in structural sparsity, where variable selection on linear transformation of parameters is of concern. This proposed scheme relaxes the linear subspace constraint to its neighborhood, often known as variable splitting in optimization. Simulation experiments can be reproduced following the Vignette. Split Knockoffs is first defined in Cao et al. (2021) <doi:10.48550/arXiv.2103.16159>.

r-senspe 1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SenSpe
Licenses: GPL 2+
Build system: r
Synopsis: Estimating Specificity at Controlled Sensitivity, or Vice Versa
Description:

Perform biomarker evaluation and comparison in terms of specificity at a controlled sensitivity level, or sensitivity at a controlled specificity level. Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023) <doi:10.5705/ss.202021.0020> for the one- and two-biomarker problems are implemented.

r-selectboost-gamlss 0.2.2
Propagated dependencies: r-selectboost@2.3.0 r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-gamlss@5.5-0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://fbertran.github.io/SelectBoost.gamlss/
Licenses: GPL 3
Build system: r
Synopsis: Stability-Selection via Correlated Resampling for 'GAMLSS' Models
Description:

Extends the SelectBoost approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) <https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

r-symmetry 0.2.3
Propagated dependencies: r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=symmetry
Licenses: Expat
Build system: r
Synopsis: Testing for Symmetry of Data and Model Residuals
Description:

Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the fGarch package). All tests are implemented using the Rcpp package which ensures great performance of the code.

r-stanza 1.0-3
Propagated dependencies: r-reticulate@1.44.1 r-nlp@0.3-2 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=stanza
Licenses: GPL 3
Build system: r
Synopsis: 'Stanza' - A 'R' NLP Package for Many Human Languages
Description:

An interface to the Python package stanza <https://stanfordnlp.github.io/stanza/index.html>. stanza is a Python NLP library for many human languages. It contains support for running various accurate natural language processing tools on 60+ languages.

r-sarima 0.9.5
Propagated dependencies: r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-polynomf@2.0-8 r-numderiv@2016.8-1.1 r-ltsa@1.4.6.1 r-lagged@0.3.2 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://geobosh.github.io/sarima/https://github.com/GeoBosh/sarima
Licenses: GPL 2+
Build system: r
Synopsis: Simulation and Prediction with Seasonal ARIMA Models
Description:

Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see <doi:10.48550/arXiv.2208.05055>, a paper on the methodology is being prepared).

r-spabundance 0.2.1
Propagated dependencies: r-rann@2.6.2 r-lme4@1.1-37 r-foreach@1.5.2 r-doparallel@1.0.17 r-coda@0.19-4.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spAbundance
Licenses: GPL 3+
Build system: r
Synopsis: Univariate and Multivariate Spatial Modeling of Species Abundance
Description:

Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.

r-sctenifoldknk 1.0.3
Propagated dependencies: r-sctenifoldnet@1.3 r-reshape2@1.4.5 r-pbapply@1.7-4 r-matrix@1.7-4 r-mass@7.3-65 r-igraph@2.2.1 r-enrichr@3.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/cailab-tamu/scTenifoldKnk
Licenses: GPL 2+
Build system: r
Synopsis: In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks
Description:

This package provides a workflow based on scTenifoldNet to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the geneâ s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the geneâ s function in the analyzed cells.

r-spsutil 0.2.2.1
Propagated dependencies: r-stringr@1.6.0 r-r6@2.6.1 r-magrittr@2.0.4 r-httr@1.4.7 r-glue@1.8.0 r-crayon@1.5.3 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/lz100/spsUtil
Licenses: GPL 3+
Build system: r
Synopsis: 'systemPipeShiny' Utility Functions
Description:

The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough.

r-sae-prop 0.1.2
Propagated dependencies: r-progress@1.2.3 r-mass@7.3-65 r-magic@1.6-1 r-fpc@2.2-13 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mrijalussholihin/sae.prop
Licenses: GPL 3
Build system: r
Synopsis: Small Area Estimation using Fay-Herriot Models with Additive Logistic Transformation
Description:

This package implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardà a M, López-Vizcaà no E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.

r-smoothroctime 0.1.1
Propagated dependencies: r-ks@1.15.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=smoothROCtime
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Smooth Time-Dependent ROC Curve Estimation
Description:

Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables.

r-scstability 1.0.3
Propagated dependencies: r-vegan@2.7-2 r-uwot@0.2.4 r-seurat@5.3.1 r-rtsne@0.17 r-rlang@1.1.6 r-pcapp@2.0-5 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-future@1.68.0 r-aricode@1.0.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scStability
Licenses: Expat
Build system: r
Synopsis: Measuring the Stability of Dimension Reduction and Cluster Assignment in scRNA-Seq Experiments
Description:

This package provides functions for evaluating the stability of low-dimensional embeddings and cluster assignments in singleâ cell RNA sequencing (scRNAâ seq) datasets. Starting from a principal component analysis (PCA) object, users can generate multiple replicates of tâ Distributed Stochastic Neighbor Embedding (tâ SNE) or Uniform Manifold Approximation and Projection (UMAP) embeddings. Embedding stability is quantified by computing pairwise Kendallâ s Tau correlations across replicates and summarizing the distribution of correlation coefficients. In addition to dimensionality reduction, scStability assesses clustering consistency using either Louvain or Leiden algorithms and calculating the Normalized Mutual Information (NMI) between all pairs of cluster assignments. For background on UMAP and t-SNE algorithms, see McInnes et al. (2020, <doi:10.21105/joss.00861>) and van der Maaten & Hinton (2008, <https://github.com/lvdmaaten/bhtsne>), respectively.

r-scoutbar 0.2.5
Propagated dependencies: r-shiny@1.11.1 r-reactr@0.6.1 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scoutbaR
Licenses: Expat
Build system: r
Synopsis: Spotlight 'React' Widget for 'shiny' Apps
Description:

This package creates a contextual menu that can be triggered with keyboard shortcuts or programmatically. This can replace traditional sidebars or navigation bars, thereby enhancing the user experience with lighter user interfaces.

r-shrinkgpr 1.1.1
Propagated dependencies: r-torch@0.16.3 r-rlang@1.1.6 r-progress@1.2.3 r-gsl@2.1-9
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=shrinkGPR
Licenses: GPL 2+
Build system: r
Synopsis: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
Description:

Efficient variational inference methods for fully Bayesian Gaussian Process Regression (GPR) models with hierarchical shrinkage priors, including the triple gamma prior for effective variable selection and covariance shrinkage in high-dimensional settings. The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.

r-switchcase 0.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/jsugarelli/switchcase/
Licenses: GPL 3
Build system: r
Synopsis: Simple and Flexible Switch-Case Construct for the 'R' Language
Description:

This package provides a switch-case construct for R', as it is known from other programming languages. It allows to test multiple, similar conditions in an efficient, easy-to-read manner, so nested if-else constructs can be avoided. The switch-case construct is designed as an R function that allows to return values depending on which condition is met and lets the programmer flexibly decide whether or not to leave the switch-case construct after a case block has been executed.

r-springer 0.1.9
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://github.com/feizhoustat/springer
Licenses: GPL 2
Build system: r
Synopsis: Sparse Group Variable Selection for Gene-Environment Interactions in the Longitudinal Study
Description:

Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for GÃ E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal GÃ E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.

r-spork 0.3.5
Propagated dependencies: r-png@0.1-8 r-latexpdf@0.1.8 r-kableextra@1.4.0 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=spork
Licenses: GPL 3
Build system: r
Synopsis: Generalized Label Formatting
Description:

The spork syntax describes label formatting concisely, supporting mixed nesting of subscripts and superscripts to arbitrary depth. It intends to be easy to read and write in plain text, and easy to convert to equivalent presentations in plotmath', latex', and html'. Greek symbols and a multiplication symbol are explicitly supported. See ?as_spork and ?as_previews.

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