_            _    _        _         _
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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-sdcnway 1.0.1
Propagated dependencies: r-rdpack@2.6.4 r-plyr@1.8.9 r-mass@7.3-65 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=SDCNway
Licenses: GPL 2
Build system: r
Synopsis: Tools to Evaluate Disclosure Risk
Description:

This package provides tools for calculating disclosure risk measures for microdata, including record-level and file-level measures. The record-level disclosure risk is estimated primarily using exhaustive tabulation. The file-level disclosure risk is estimated by fitting loglinear models on the observed sample counts in cells formed by key variables and their interactions. Funded by the National Center for Education Statistics. See Skinner and Shlomo (2008) <doi:10.1198/016214507000001328> for a description of the file-level risk measures and the loglinear model approach.

r-str 0.7.1
Propagated dependencies: r-sparsem@1.84-2 r-quantreg@6.1 r-matrix@1.7-4 r-forecast@8.24.0 r-foreach@1.5.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://pkg.robjhyndman.com/stR/
Licenses: GPL 3
Build system: r
Synopsis: Seasonal Trend Decomposition Using Regression
Description:

This package provides methods for decomposing seasonal data: STR (a Seasonal-Trend time series decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can also be used for forecasting.

r-sbim 1.0.0
Propagated dependencies: 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=sbim
Licenses: GPL 3+
Build system: r
Synopsis: Simulation-Based Inference using a Metamodel for Log-Likelihood Estimator
Description:

Parameter inference methods for models defined implicitly using a random simulator. Inference is carried out using simulation-based estimates of the log-likelihood of the data. The inference methods implemented in this package are explained in Park, J. (2025) <doi:10.48550/arxiv.2311.09446>. These methods are built on a simulation metamodel which assumes that the estimates of the log-likelihood are approximately normally distributed with the mean function that is locally quadratic around its maximum. Parameter estimation and uncertainty quantification can be carried out using the ht() function (for hypothesis testing) and the ci() function (for constructing a confidence interval for one-dimensional parameters).

r-sparsesurv 0.1.1
Dependencies: jags@4.3.1
Propagated dependencies: r-r2jags@0.8-9 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/alexangelakis-ang/sparsesurv
Licenses: GPL 3+
Build system: r
Synopsis: Forecasting and Early Outbreak Detection for Sparse Count Data
Description:

This package provides functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics.

r-spatialcatalogueviewer 0.1.3
Propagated dependencies: r-shinythemes@1.2.0 r-shiny@1.11.1 r-leaflet-extras@2.0.1 r-leaflet@2.2.3 r-dt@0.34.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/sebastien-plutniak/spatialCatalogueViewer
Licenses: GPL 3
Build system: r
Synopsis: 'Shiny' Tool to Create Interactive Catalogues for Geospatial Data
Description:

Seamlessly create interactive online catalogues for geospatial data. Items can be mapped as points or areas and retrieved using either a map or a dynamic table with search form and optional column filters.

r-shinywizard 1.1.3.11
Propagated dependencies: r-rstudioapi@0.17.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ShinyWizard
Licenses: GPL 3+
Build system: r
Synopsis: An Interactive Wizard to Design, Build, and Deploy R Packages Demo Presentation
Description:

Design, build, and deploy R packages demo presentations by an interactive wizard. Set up unique title, logo and themes. Add personalized tabs exposing applicability. And deploy as a part of a package or an independent app.

r-seasonder 0.2.8
Propagated dependencies: r-zoo@1.8-14 r-yaml@2.3.10 r-whisker@0.4.1 r-uuid@1.2-1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-slider@0.3.3 r-rlang@1.1.6 r-purrr@1.2.0 r-pracma@2.4.6 r-magrittr@2.0.4 r-lubridate@1.9.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-geosphere@1.5-20 r-dplyr@1.1.4 r-data-table@1.17.8 r-constants@2022.0 r-bitops@1.0-9 r-bit64@4.6.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/GOFUVI/SeaSondeR
Licenses: GPL 3+
Build system: r
Synopsis: Radial Metrics from SeaSonde HF-Radar Data
Description:

Read CODAR's SeaSonde High-Frequency Radar spectra files, compute radial metrics, and generate plots for spectra and antenna pattern data. Implementation is based in technical manuals, publications and patents, please refer to the following documents for more information: Barrick and Lipa (1999) <https://codar.com/images/about/patents/05990834.PDF>; CODAR Ocean Sensors (2002) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/Informative/FirstOrder_Settings.pdf>; Lipa et al. (2006) <doi:10.1109/joe.2006.886104>; Paolo et al. (2007) <doi:10.1109/oceans.2007.4449265>; CODAR Ocean Sensors (2009a) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/GuidesToFileFormats/File_AntennaPattern.pdf>; CODAR Ocean Sensors (2009b) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/GuidesToFileFormats/File_CrossSpectraReduced.pdf>; CODAR Ocean Sensors (2016a) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/File_Formats/File_Cross_Spectra_V6.pdf>; CODAR Ocean Sensors (2016b) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/File_Formats/FIle_Reduced_Spectra.pdf>; CODAR Ocean Sensors (2016c) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/Application_Guides/Guide_SpectraPlotterMap.pdf>; Bushnell and Worthington (2022) <doi:10.25923/4c5x-g538>.

r-saros-base 1.2.0
Propagated dependencies: r-zip@2.3.3 r-yaml@2.3.10 r-vctrs@0.6.5 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-stringi@1.8.7 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-glue@1.8.0 r-fs@1.6.6 r-forcats@1.0.1 r-dplyr@1.1.4 r-cli@3.6.5 r-bcrypt@1.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://nifu-no.github.io/saros.base/
Licenses: Expat
Build system: r
Synopsis: Base Tools for Semi-Automatic Reporting of Ordinary Surveys
Description:

Scaffold an entire web-based report using template chunks, based on a small chapter overview and a dataset. Highly adaptable with prefixes, suffixes, translations, etc. Also contains tools for password-protecting, e.g. for each organization's report on a website. Developed for the common case of a survey across multiple organizations/sites where each organization wants to obtain results for their organization compared with everyone else. See saros (<https://CRAN.R-project.org/package=saros>) for tools used for authors in the drafted reports.

r-secfish 0.1.7
Propagated dependencies: r-optimization@1.0-9 r-hmisc@5.2-4 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=SECFISH
Licenses: GPL 2
Build system: r
Synopsis: Disaggregate Variable Costs
Description:

These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.

r-syn 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://syn.njtierney.com/
Licenses: GPL 3
Build system: r
Synopsis: Creates Synonyms From Target Words
Description:

Generates synonyms from a given word drawing from a synonym list from the moby project <http://moby-thesaurus.org/>.

r-stgam 1.2.0
Propagated dependencies: r-stringr@1.6.0 r-purrr@1.2.0 r-mgcv@1.9-4 r-magrittr@2.0.4 r-glue@1.8.0 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://github.com/lexcomber/stgam
Licenses: Expat
Build system: r
Synopsis: Spatially and Temporally Varying Coefficient Models Using Generalized Additive Models
Description:

This package provides a framework for undertaking space and time varying coefficient models (varying parameter models) using a Generalized Additive Model (GAM) with smooths approach. The framework suggests the need to investigate for the presence and nature of any space-time dependencies in the data. It proposes a workflow that creates and refines an initial space-time GAM and includes tools to create and evaluate multiple model forms. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Create all possible space and/or time models in which each predictor is specified in different ways in smooths. iii) Evaluate each model via their AIC value and pick the best one. iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. The number of knots used in each smooth can be specified directly or iteratively increased. This is illustrated with a climate point dataset of the dry rain forest in South America. This builds on work in Comber et al (2024) <doi:10.1080/13658816.2023.2270285> and Comber et al (2004) <doi:10.3390/ijgi13120459>.

r-scryr 1.0.0
Propagated dependencies: r-tibble@3.3.0 r-purrr@1.2.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://curso-r.github.io/scryr/
Licenses: Expat
Build system: r
Synopsis: An Interface to the 'Scryfall' API
Description:

This package provides a simple, light, and robust interface between R and the Scryfall card data API <https://scryfall.com/docs/api>.

r-selectiongain 2.0.710
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=selectiongain
Licenses: GPL 2
Build system: r
Synopsis: Tool for Calculation and Optimization of the Expected Gain from Multi-Stage Selection
Description:

Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.

r-smfilter 1.0.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/yukai-yang/SMFilter
Licenses: GPL 3
Build system: r
Synopsis: Filtering Algorithms for the State Space Models on the Stiefel Manifold
Description:

This package provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.

r-swatches 0.5.0
Propagated dependencies: r-xml2@1.5.0 r-stringr@1.6.0 r-pack@0.1-2 r-httr@1.4.7 r-colorspace@2.1-2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/hrbrmstr/swatches
Licenses: Expat
Build system: r
Synopsis: Read, Inspect, and Manipulate Color Swatch Files
Description:

There are numerous places to create and download color palettes. These are usually shared in Adobe swatch file formats of some kind. There is also often the need to use standard palettes developed within an organization to ensure that aesthetics are carried over into all projects and output. Now there is a way to read these swatch files in R and avoid transcribing or converting color values by hand or or with other programs. This package provides functions to read and inspect Adobe Color ('ACO'), Adobe Swatch Exchange ('ASE'), GIMP Palette ('GPL'), OpenOffice palette ('SOC') files and KDE Palette ('colors') files. Detailed descriptions of Adobe Color and Swatch Exchange file formats as well as other swatch file formats can be found at <http://www.selapa.net/swatches/colors/fileformats.php>.

r-soilr 1.2.107
Propagated dependencies: r-sets@1.0-25 r-purrr@1.2.0 r-igraph@2.2.1 r-expm@1.0-0 r-desolve@1.40 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SoilR
Licenses: GPL 3
Build system: r
Synopsis: Models of Soil Organic Matter Decomposition
Description:

This package provides functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.

r-sparser 0.3.2
Propagated dependencies: r-rlang@1.1.6 r-recipes@1.3.1 r-ncvreg@3.16.0 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://petersonr.github.io/sparseR/
Licenses: GPL 3
Build system: r
Synopsis: Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials
Description:

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.

r-shutterplot 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=shutterplot
Licenses: GPL 3
Build system: r
Synopsis: The R Shutter Plot Package
Description:

Shows the scatter plot along with the fitted regression lines. It depicts min, max, the three quartiles, mean, and sd for each variable. It also depicts sd-line, sd-box, r, r-square, prediction boundaries, and regression outliers.

r-spsurv 1.0.0
Propagated dependencies: r-survival@3.8-3 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-mass@7.3-65 r-loo@2.8.0 r-coda@0.19-4.1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spsurv
Licenses: GPL 3
Build system: r
Synopsis: Bernstein Polynomial Based Semiparametric Survival Analysis
Description:

This package provides a set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. spsurv includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.

r-spldv 0.1.3
Propagated dependencies: r-sphet@2.1-1 r-spatialreg@1.4-2 r-numderiv@2016.8-1.1 r-memisc@0.99.31.8.3 r-maxlik@1.5-2.1 r-matrix@1.7-4 r-mass@7.3-65 r-formula@1.2-5 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/gpiras/spldv
Licenses: GPL 2+
Build system: r
Synopsis: Spatial Models for Limited Dependent Variables
Description:

The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators <doi:10.18637/jss.v107.i08>. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) <doi:10.1016/j.jocm.2023.100432>.

r-spacenet 1.2
Propagated dependencies: r-vegan@2.7-2 r-sna@2.8 r-rfast@2.1.5.2 r-rcpptn@0.2-2 r-permute@0.9-8 r-mclust@6.1.2 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=spaceNet
Licenses: GPL 2+
Build system: r
Synopsis: Latent Space Models for Multidimensional Networks
Description:

Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.

r-sparsereg 1.2
Propagated dependencies: r-vgam@1.1-13 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-msm@1.8.2 r-mcmcpack@1.7-1 r-mass@7.3-65 r-gridextra@2.3 r-glmnet@4.1-10 r-gigrvg@0.8 r-ggplot2@4.0.1 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sparsereg
Licenses: GPL 2+
Build system: r
Synopsis: Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data
Description:

Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects. This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots. The method can implement sparse linear regression and sparse probit regression. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to models with truncated outcomes, propensity score, and instrumental variable analysis.

r-spdownscale 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spdownscale
Licenses: GPL 2
Build system: r
Synopsis: Spatial Downscaling Using Bias Correction Approach
Description:

Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.

r-sqmtools 1.7.2
Propagated dependencies: r-zip@2.3.3 r-reshape2@1.4.5 r-pathview@1.50.0 r-ggplot2@4.0.1 r-data-table@1.17.8 r-biostrings@2.78.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/jtamames/SqueezeMeta
Licenses: GPL 3
Build system: r
Synopsis: Analyze Results Generated by the 'SqueezeMeta' Pipeline
Description:

SqueezeMeta is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (<https://github.com/jtamames/SqueezeMeta>). This package provides functions loading SqueezeMeta results into R, filtering them based on different criteria, and visualizing the results using basic plots. The SqueezeMeta project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as vegan or DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) <doi:10.1186/s12859-020-03703-2>.

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