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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
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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-spiga 1.0.0
Propagated dependencies: r-ga@3.2.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SPIGA
Licenses: GPL 2
Build system: r
Synopsis: Compute SPI Index using the Methods Genetic Algorithm and Maximum Likelihood
Description:

Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.

r-socviz 1.2
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-fs@1.6.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://kjhealy.github.io/socviz/
Licenses: Expat
Build system: r
Synopsis: Utility Functions and Data Sets for Data Visualization
Description:

Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) <ISBN 978-0691181622>.

r-sstvars 1.2.3
Dependencies: lapack@3.12.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pbapply@1.7-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/saviviro/sstvars
Licenses: GPL 3
Build system: r
Synopsis: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models
Description:

Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, counterfactual analysis, and computation of impulse response functions, generalized impulse response functions, generalized forecast error variance decompositions, as well as historical decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.1016/j.jedc.2025.105162>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

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-str2str 1.0.0
Propagated dependencies: r-reshape@0.8.10 r-plyr@1.8.9 r-checkmate@2.3.3 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=str2str
Licenses: GPL 2+
Build system: r
Synopsis: Convert R Objects from One Structure to Another
Description:

Offers a suite of functions for converting to and from (atomic) vectors, matrices, data.frames, and (3D+) arrays as well as lists of these objects. It is an alternative to the base R as.<str>.<method>() functions (e.g., as.data.frame.array()) that provides more useful and/or flexible restructuring of R objects. To do so, it only works with common structuring of R objects (e.g., data.frames with only atomic vector columns).

r-stppsim 1.3.4
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-terra@1.8-86 r-stringr@1.6.0 r-splancs@2.01-45 r-spatstat-geom@3.6-1 r-sparr@2.3-16 r-sp@2.2-0 r-simriv@1.0.7 r-sf@1.0-23 r-raster@3.6-32 r-progressr@0.18.0 r-otusummary@0.1.2 r-magrittr@2.0.4 r-lubridate@1.9.4 r-leaflet@2.2.3 r-ks@1.15.1 r-gstat@2.1-4 r-ggplot2@4.0.1 r-geosphere@1.5-20 r-future-apply@1.20.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-cowplot@1.2.0 r-chron@2.3-62
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/MAnalytics/stppSim
Licenses: GPL 3
Build system: r
Synopsis: Spatiotemporal Point Patterns Simulation
Description:

Generates artificial point patterns marked by their spatial and temporal signatures. The resulting point cloud may exhibit inherent interactions between both signatures. The simulation integrates microsimulation (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) and agent-based models (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>), beginning with the configuration of movement characteristics for the specified agents (referred to as walkers') and their interactions within the simulation environment. These interactions (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) result in specific spatiotemporal patterns that can be visualized, analyzed, and used for various analytical purposes. Given the growing scarcity of detailed spatiotemporal data across many domains, this package provides an alternative data source for applications in social and life sciences.

r-survrm2adapt 1.1.0
Propagated dependencies: r-survival@3.8-3 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=survRM2adapt
Licenses: GPL 2
Build system: r
Synopsis: Flexible and Coherent Test/Estimation Procedure Based on Restricted Mean Survival Times
Description:

Estimates the restricted mean survival time (RMST) with the time window [0, tau], where tau is adaptively selected from the procedure, proposed by Horiguchi et al. (2018) <doi:10.1002/sim.7661>. It also estimates the RMST with the time window [tau1, tau2], where tau1 is adaptively selected from the procedure, proposed by Horiguchi et al. (2023) <doi:10.1002/sim.9662>.

r-saltsampler 1.1.0
Propagated dependencies: r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SALTSampler
Licenses: Modified BSD
Build system: r
Synopsis: Efficient Sampling on the Simplex
Description:

The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC) sampling of random variables on a simplex. A Self-Adjusting Logit Transform (SALT) proposal is used so that sampling is still efficient even in difficult cases, such as those in high dimensions or with parameters that differ by orders of magnitude. Special care is also taken to maintain accuracy even when some coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are included in the package, enabling easy assessment of the convergence and mixing of the chain within the constrained space.

r-ssev 0.1.0
Propagated dependencies: r-pwr@1.3-0 r-mess@0.6.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ssev
Licenses: GPL 3
Build system: r
Synopsis: Sample Size Computation for Fixed N with Optimal Reward
Description:

Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.

r-smash 1.0.0
Propagated dependencies: r-smarter@1.0.1 r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 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=SMASH
Licenses: GPL 3+
Build system: r
Synopsis: Subclone Multiplicity Allocation and Somatic Heterogeneity
Description:

Cluster user-supplied somatic read counts with corresponding allele-specific copy number and tumor purity to infer feasible underlying intra-tumor heterogeneity in terms of number of subclones, multiplicity, and allocation (Little et al. (2019) <doi:10.1186/s13073-019-0643-9>).

r-sabre 0.4.3
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-sf@1.0-23 r-rlang@1.1.6 r-raster@3.6-32 r-entropy@1.3.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://jakubnowosad.com/sabre/
Licenses: Expat
Build system: r
Synopsis: Spatial Association Between Regionalizations
Description:

Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).

r-spatialreg-hp 0.0-1
Propagated dependencies: r-vegan@2.7-2 r-spdep@1.4-1 r-spatialreg@1.4-2 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=spatialreg.hp
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Hierarchical Partitioning of R2 for Spatial Simultaneous Autoregressive Model
Description:

Conducts hierarchical partitioning to calculate individual contributions of spatial and predictors (groups) towards total R2 for spatial simultaneous autoregressive model.

r-sate 3.1.0
Propagated dependencies: r-survey@4.4-8 r-mass@7.3-65 r-ellipse@0.5.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sate
Licenses: CC0
Build system: r
Synopsis: Scientific Analysis of Trial Errors (SATE)
Description:

Bundles functions used to analyze the harmfulness of trial errors in criminal trials. Functions in the Scientific Analysis of Trial Errors ('sate') package help users estimate the probability that a jury will find a defendant guilty given jurors preferences for a guilty verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical trial conditions to conduct harmful error analysis. The conceptual framework is discussed by Barry Edwards, A Scientific Framework for Analyzing the Harmfulness of Trial Errors, UCLA Criminal Justice Law Review (2024) <doi:10.5070/CJ88164341> and Barry Edwards, If The Jury Only Knew: The Effect Of Omitted Mitigation Evidence On The Probability Of A Death Sentence, Virginia Journal of Social Policy & the Law (2025) <https://vasocialpolicy.org/wp-content/uploads/2025/05/Edwards-If-The-Jury-Only-Knew.pdf>. The relationship between individual jurors verdict preferences and the probability that a jury returns a guilty verdict has been studied by Davis (1973) <doi:10.1037/h0033951>; MacCoun & Kerr (1988) <doi:10.1037/0022-3514.54.1.21>, and Devine et el. (2001) <doi:10.1037/1076-8971.7.3.622>, among others.

r-seminr 2.4.0
Propagated dependencies: r-webp@1.3.0 r-testthat@3.3.0 r-rmarkdown@2.30 r-lavaan@0.6-20 r-knitr@1.50 r-glue@1.8.0 r-diagrammersvg@0.1 r-diagrammer@1.0.11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/sem-in-r/seminr
Licenses: GPL 3
Build system: r
Synopsis: Building and Estimating Structural Equation Models
Description:

This package provides a powerful, easy to use syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 <doi:10.2139/ssrn.3900621>).

r-stemmatology 0.3.2
Propagated dependencies: r-xml2@1.5.0 r-igraph@2.2.1 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/Jean-Baptiste-Camps/stemmatology
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Stemmatological Analysis of Textual Traditions
Description:

Explore and analyse the genealogy of textual or musical traditions, from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by Camps and Cafiero (2015) <doi:10.1484/M.LECTIO-EB.5.102565>.

r-smer 0.0.2
Propagated dependencies: r-tidyr@1.3.1 r-testthat@3.3.0 r-rhdf5lib@1.32.0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-mvmapit@2.0.3 r-logging@0.10-108 r-highfive@3.3.0 r-genio@1.1.2 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/lcrawlab/sme
Licenses: Expat
Build system: r
Synopsis: Sparse Marginal Epistasis Test
Description:

The Sparse Marginal Epistasis Test is a computationally efficient genetics method which detects statistical epistasis in complex traits; see Stamp et al. (2025, <doi:10.1101/2025.01.11.632557>) for details.

r-selcorr 1.0
Propagated dependencies: 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=selcorr
Licenses: GPL 3
Build system: r
Synopsis: Post-Selection Inference for Generalized Linear Models
Description:

Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models.

r-stabiliser 1.0.6
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rsample@1.3.1 r-recipes@1.3.1 r-purrr@1.2.0 r-ncvreg@3.16.0 r-matrixstats@1.5.0 r-lmertest@3.1-3 r-lme4@1.1-37 r-knitr@1.50 r-hmisc@5.2-4 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-expss@0.11.7 r-dplyr@1.1.4 r-caret@7.0-1 r-broom@1.0.10 r-bigstep@1.1.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=stabiliser
Licenses: Expat
Build system: r
Synopsis: Stabilising Variable Selection
Description:

This package provides a stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.

r-spectrino 2.0.0
Propagated dependencies: r-jsonlite@2.0.0 r-httpuv@1.6.16
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://www.spectrino.com
Licenses: GPL 2+
Build system: r
Synopsis: Spectra Viewer, Organizer, Data Preparation and Property Blocks
Description:

Spectra viewer, organizer, data preparation and property blocks from within R or stand-alone. Binary (application) part is installed separately using spnInstallApp() from spectrino package.

r-silggm 1.0.0
Propagated dependencies: r-reshape@0.8.10 r-rcpp@1.1.0 r-mass@7.3-65 r-glasso@1.11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SILGGM
Licenses: GPL 2+
Build system: r
Synopsis: Statistical Inference of Large-Scale Gaussian Graphical Model in Gene Networks
Description:

This package provides a general framework to perform statistical inference of each gene pair and global inference of whole-scale gene pairs in gene networks using the well known Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM estimation with false discovery rate control (FDR) using scaled Lasso or Lasso (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install Rtools before the installation of this package.

r-stcos 0.3.1
Propagated dependencies: r-sf@1.0-23 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/holans/ST-COS
Licenses: Expat
Build system: r
Synopsis: Space-Time Change of Support
Description:

Spatio-temporal change of support (STCOS) methods are designed for statistical inference on geographic and time domains which differ from those on which the data were observed. In particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The stcos package contains tools which facilitate use of STCOS models.

r-selfcontrolledcaseseries 6.1.1
Propagated dependencies: r-sqlrender@1.19.4 r-resultmodelmanager@0.6.2 r-readr@2.1.6 r-rcpp@1.1.0 r-r6@2.6.1 r-parallellogger@3.5.1 r-jsonlite@2.0.0 r-ggplot2@4.0.1 r-empiricalcalibration@3.1.4 r-dplyr@1.1.4 r-digest@0.6.39 r-databaseconnector@7.1.0 r-cyclops@3.6.0 r-checkmate@2.3.3 r-andromeda@1.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://ohdsi.github.io/SelfControlledCaseSeries/
Licenses: ASL 2.0
Build system: r
Synopsis: Self-Controlled Case Series
Description:

Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

r-stors 1.0.1
Propagated dependencies: r-rlang@1.1.6 r-microbenchmark@1.5.0 r-digest@0.6.39 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://ahmad-alqabandi.github.io/stors/
Licenses: Expat
Build system: r
Synopsis: Step Optimised Rejection Sampling
Description:

Fast and efficient sampling from general univariate probability density functions. Implements a rejection sampling approach designed to take advantage of modern CPU caches and minimise evaluation of the target density for most samples. Many standard densities are internally implemented in C for high performance, with general user defined densities also supported. A paper describing the methodology will be released soon.

r-smartdesign 0.74
Propagated dependencies: r-knitr@1.50
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=smartDesign
Licenses: GPL 3+
Build system: r
Synopsis: Sequential Multiple Assignment Randomized Trial Design
Description:

SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021) <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.

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