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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-stellar 0.3-6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://astro.df.unipi.it/stellar-models/
Licenses: GPL 2+
Build system: r
Synopsis: Evolutionary Tracks and Isochrones from Pisa Stellar Evolution Database
Description:

Manages and display stellar tracks and isochrones from Pisa low-mass database. Includes tools for isochrones construction and tracks interpolation.

r-sim-diffproc 5.0
Propagated dependencies: r-mass@7.3-65 r-deriv@4.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/acguidoum/Sim.DiffProc
Licenses: GPL 2+
Build system: r
Synopsis: Simulation of Diffusion Processes
Description:

It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.

r-shelf 1.12.1
Propagated dependencies: r-tidyr@1.3.1 r-survminer@0.5.1 r-survival@3.8-3 r-sn@2.1.1 r-shinymatrix@0.8.1 r-shiny@1.11.1 r-scales@1.4.0 r-rmarkdown@2.30 r-hmisc@5.2-4 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-ggextra@0.11.0 r-flexsurv@2.3.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/OakleyJ/SHELF
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Tools to Support the Sheffield Elicitation Framework
Description:

This package implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included.

r-simule 1.3.0
Propagated dependencies: r-pcapp@2.0-5 r-lpsolve@5.6.23 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/QData/SIMULE
Licenses: GPL 2
Build system: r
Synopsis: Constrained L1 Minimization Approach for Estimating Multiple Sparse Gaussian or Nonparanormal Graphical Models
Description:

This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.

r-stevedata 1.8.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://svmiller.com/stevedata/
Licenses: GPL 2
Build system: r
Synopsis: Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics
Description:

This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy.

r-scenfire 0.1.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-terra@1.8-86 r-stringr@1.6.0 r-sf@1.0-23 r-rlang@1.1.6 r-readr@2.1.6 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 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=scenfire
Licenses: Expat
Build system: r
Synopsis: Post-Processing Algorithm for Integrating Wildfire Simulations
Description:

This package provides a specialized selection algorithm designed to align simulated fire perimeters with specific fire size distribution scenarios. The foundation of this approach lies in generating a vast collection of plausible simulated fires across a wide range of conditions, assuming a random pattern of ignition. The algorithm then assembles individual fire perimeters based on their specific probabilities of occurrence, e.g., determined by (i) the likelihood of ignition and (ii) the probability of particular fire-weather scenarios, including wind speed and direction. Implements the method presented in Rodrigues (2025a) <doi:10.5194/egusphere-egu25-8974>. Demo data and code examples can be found in Rodrigues (2025b) <doi:10.5281/zenodo.15282605>.

r-svplots 0.1.0
Propagated dependencies: 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=svplots
Licenses: GPL 3
Build system: r
Synopsis: Sample Variance Plots (Sv-Plots)
Description:

Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating the squared deviations from sample variance. Besides indicating the contribution of squared deviations for the sample variability, these plots are capable of detecting characteristics of the distribution such as symmetry, skewness and outliers. A remarkable graphical method based on Sv-plot2 can determine the decision on testing hypotheses over one or two population means. In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.

r-simico 0.2.0
Propagated dependencies: r-icskat@0.3.0 r-fastghquad@1.0.1 r-compquadform@1.4.4 r-bindata@0.9-24
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SIMICO
Licenses: GPL 3
Build system: r
Synopsis: Set-Based Inference for Multiple Interval-Censored Outcomes
Description:

This package contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points.

r-survpresmooth 1.1-12
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=survPresmooth
Licenses: GPL 2+
Build system: r
Synopsis: Presmoothed Estimation in Survival Analysis
Description:

Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.

r-smoothie 1.0-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=smoothie
Licenses: GPL 2+
Build system: r
Synopsis: Two-Dimensional Field Smoothing
Description:

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).

r-spectralr 0.1.4
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-sf@1.0-23 r-rlang@1.1.6 r-rgee@1.1.8 r-reshape2@1.4.5 r-ggplot2@4.0.1 r-geojsonio@0.11.3 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/olehprylutskyi/spectralR/
Licenses: GPL 3
Build system: r
Synopsis: Obtain and Visualize Spectral Reflectance Data for Earth Surface Polygons
Description:

This package provides tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service (<https://earthengine.google.com/>) and used as a source of spectral data.

r-saws 0.9-7.0
Propagated dependencies: r-gee@4.13-29
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=saws
Licenses: GPL 2+
Build system: r
Synopsis: Small-Sample Adjustments for Wald Tests Using Sandwich Estimators
Description:

Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.

r-sparsedc 0.1.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SparseDC
Licenses: GPL 3
Build system: r
Synopsis: Implementation of SparseDC Algorithm
Description:

This package implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.

r-simplace 5.1.2
Dependencies: openjdk@25
Propagated dependencies: r-rjava@1.0-11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/gk-crop/simplace_rpkg/
Licenses: GPL 2
Build system: r
Synopsis: Interface to Use the Modelling Framework 'SIMPLACE'
Description:

Interface to interact with the modelling framework SIMPLACE and to parse the results of simulations.

r-snha 0.1.3
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mittelmark/snha
Licenses: Expat
Build system: r
Synopsis: Creating Correlation Networks using St. Nicolas House Analysis
Description:

Create correlation networks using St. Nicolas House Analysis ('SNHA'). The package can be used for visualizing multivariate data similar to Principal Component Analysis or Multidimensional Scaling using a ranking approach. In contrast to MDS and PCA', SNHA uses a network approach to explore interacting variables. For details see Hermanussen et. al. 2021', <doi:10.3390/ijerph18041741>.

r-sslfmm 0.1.0
Propagated dependencies: r-mvtnorm@1.3-3 r-matrixstats@1.5.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SSLfmm
Licenses: GPL 3
Build system: r
Synopsis: Semi-Supervised Learning under a Mixed-Missingness Mechanism in Finite Mixture Models
Description:

This package implements a semi-supervised learning framework for finite mixture models under a mixed-missingness mechanism. The approach models both missing completely at random (MCAR) and entropy-based missing at random (MAR) processes using a logisticâ entropy formulation. Estimation is carried out via an Expectationâ -Conditional Maximisation (ECM) algorithm with robust initialisation routines for stable convergence. The methodology relates to the statistical perspective and informative missingness behaviour discussed in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package provides functions for data simulation, model estimation, prediction, and theoretical Bayes error evaluation for analysing partially labelled data under a mixed-missingness mechanism.

r-spaths 1.2.0
Propagated dependencies: r-rcpp@1.1.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/cdueben/spaths
Licenses: Expat
Build system: r
Synopsis: Shortest Paths Between Points in Grids
Description:

Shortest paths between points in grids. Optional barriers and custom transition functions. Applications regarding planet Earth, as well as generally spheres and planes. Optimized for computational performance, customizability, and user friendliness. Graph-theoretical implementation tailored to gridded data. Currently focused on Dijkstra's (1959) <doi:10.1007/BF01386390> algorithm. Future updates broaden the scope to other least cost path algorithms and to centrality measures.

r-sakura 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://shikokuchuo.net/sakura/
Licenses: GPL 3+
Build system: r
Synopsis: Extension to R Serialization
Description:

Extends the functionality of R serialization by augmenting the built-in reference hook system. This enhanced implementation allows optimal, one-pass integrated serialization that combines R serialization with third-party serialization methods. Facilitates the serialization of even complex R objects, which contain non-system reference objects, such as those accessed via external pointers, for use in parallel and distributed computing.

r-stabilo 0.1.1
Propagated dependencies: r-pracma@2.4.6 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=stabilo
Licenses: GPL 3
Build system: r
Synopsis: Stabilometric Signal Quantification
Description:

This package provides functions for stabilometric signal quantification. The input is a data frame containing the x, y coordinates of the center-of-pressure displacement. Jose Magalhaes de Oliveira (2017) <doi:10.3758/s13428-016-0706-4> "Statokinesigram normalization method"; T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) <doi:10.1109/10.532130> "Measures of postural steadiness: Differences between healthy young and elderly adults"; L F Oliveira et al (1996) <doi:10.1088/0967-3334/17/4/008> "Calculation of area of stabilometric signals using principal component analisys".

r-stortingscrape 0.4.1
Propagated dependencies: r-stringr@1.6.0 r-rvest@1.0.5 r-httr2@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/martigso/stortingscrape
Licenses: GPL 3+
Build system: r
Synopsis: Access Data from the Norwegian Parliament API
Description:

This package provides functions for retrieving general and specific data from the Norwegian Parliament, through the Norwegian Parliament API at <https://data.stortinget.no>.

r-smahp 0.0.5
Propagated dependencies: r-survival@3.8-3 r-penaft@0.3.2 r-ncvreg@3.16.0 r-glmnet@4.1-10 r-fdrtool@1.2.18 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=SMAHP
Licenses: GPL 3
Build system: r
Synopsis: Survival Mediation Analysis of High-Dimensional Proteogenomic Data
Description:

SMAHP (pronounced as SOO-MAP) is a novel multi-omics framework for causal mediation analysis of high-dimensional proteogenomic data with survival outcomes. The full methodological details can be found in our recent preprint by Ahn S et al. (2025) <doi:10.48550/arXiv.2503.08606>.

r-spmoran 0.3.3
Propagated dependencies: r-vegan@2.7-2 r-spdep@1.4-1 r-sf@1.0-23 r-rcolorbrewer@1.1-3 r-rarpack@0.11-0 r-matrix@1.7-4 r-ggplot2@4.0.1 r-foreach@1.5.2 r-fnn@1.1.4.1 r-fields@17.1 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dmuraka/spmoran
Licenses: GPL 2+
Build system: r
Synopsis: Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors
Description:

This package provides a collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi: 10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).

r-scuba 1.11-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scuba
Licenses: GPL 2+
Build system: r
Synopsis: Diving Calculations and Decompression Models
Description:

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

r-stats19 3.4.0
Propagated dependencies: r-sf@1.0-23 r-readr@2.1.6 r-lubridate@1.9.4 r-jsonlite@2.0.0 r-dplyr@1.1.4 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/ropensci/stats19
Licenses: GPL 3
Build system: r
Synopsis: Work with Open Road Traffic Casualty Data from Great Britain
Description:

Work with and download road traffic casualty data from Great Britain. Enables access to the UK's official road safety statistics, STATS19'. Enables users to specify a download directory for the data, which can be set permanently by adding `STATS19_DOWNLOAD_DIRECTORY=/path/to/a/dir` to your `.Renviron` file, which can be opened with `usethis::edit_r_environ()`. The data is provided as a series of `.csv` files. This package downloads, reads-in and formats the data, making it suitable for analysis. See the stats19 vignette for details. Data available from 1979 to 2024. See the official data series at <https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-accidents-safety-data>. The package is described in a paper in the Journal of Open Source Software (Lovelace et al. 2019) <doi:10.21105/joss.01181>. See Gilardi et al. (2022) <doi:10.1111/rssa.12823>, Vidal-Tortosa et al. (2021) <doi:10.1016/j.jth.2021.101291>, Tait et al. (2023) <doi:10.1016/j.aap.2022.106895>, and León et al. (2025) <doi:10.18637/jss.v114.i09> for examples of how the data can be used for methodological and empirical research.

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