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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 search send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-statisfactory 1.0.4
Propagated dependencies: r-omnibus@1.2.15
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/adamlilith/statisfactory
Licenses: GPL 3+
Build system: r
Synopsis: Statistical and Geometrical Tools
Description:

This package provides a collection of statistical and geometrical tools including the aligned rank transform (ART; Higgins et al. 1990 <doi:10.4148/2475-7772.1443>; Peterson 2002 <doi:10.22237/jmasm/1020255240>; Wobbrock et al. 2011 <doi:10.1145/1978942.1978963>), 2-D histograms and histograms with overlapping bins, a function for making all possible formulae within a set of constraints, amongst others.

r-sensemakr 0.1.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/carloscinelli/sensemakr
Licenses: GPL 3
Build system: r
Synopsis: Sensitivity Analysis Tools for Regression Models
Description:

This package implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

r-strategicplayers 1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=strategicplayers
Licenses: GPL 3
Build system: r
Synopsis: Strategic Players
Description:

Identifies individuals in a social network who should be the intervention subjects for a network intervention in which you have a group of targets, a group of avoiders, and a group that is neither.

r-stppsim 1.3.4
Propagated dependencies: r-tidyr@1.3.2 r-tibble@3.3.1 r-terra@1.9-27 r-stringr@1.6.0 r-splancs@2.01-45 r-spatstat-geom@3.7-3 r-sparr@2.3-16 r-sp@2.2-1 r-simriv@1.0.7 r-sf@1.1-1 r-raster@3.6-32 r-progressr@0.19.0 r-otusummary@0.1.2 r-magrittr@2.0.5 r-lubridate@1.9.5 r-leaflet@2.2.3 r-ks@1.15.2 r-gstat@2.1-6 r-ggplot2@4.0.3 r-geosphere@1.6-8 r-future-apply@1.20.2 r-dplyr@1.2.1 r-data-table@1.18.4 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-supmz 0.2.0
Propagated dependencies: r-magrittr@2.0.5 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/myaseen208/SupMZ
Licenses: GPL 2
Build system: r
Synopsis: Detecting Structural Change with Heteroskedasticity
Description:

Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).

r-sasquatch 0.1.3
Dependencies: python@3.12.12
Propagated dependencies: r-rstudioapi@0.18.0 r-rlang@1.2.0 r-reticulate@1.46.0 r-knitr@1.51 r-htmlwidgets@1.6.4 r-evaluate@1.0.5 r-cli@3.6.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://docs.ropensci.org/sasquatch/
Licenses: Expat
Build system: r
Synopsis: Use 'SAS', R, and 'quarto' Together
Description:

Use R and SAS within reproducible multilingual quarto documents. Run SAS code blocks interactively, send data back and forth between SAS and R, and render SAS output within quarto documents. SAS connections are established through a combination of SASPy and reticulate'.

r-surveydown 1.3.0
Propagated dependencies: r-yaml@2.3.12 r-xml2@1.5.2 r-shinywidgets@0.9.1 r-shinyjs@2.1.1 r-shiny@1.13.0 r-rvest@1.0.5 r-rstudioapi@0.18.0 r-rpostgres@1.4.10 r-rmarkdown@2.31 r-quarto@1.5.1 r-pool@1.0.5 r-miniui@0.1.2 r-markdown@2.0 r-jsonlite@2.0.0 r-htmltools@0.5.9 r-fs@2.1.0 r-dotenv@1.0.3 r-dbi@1.3.0 r-cli@3.6.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://pkg.surveydown.org
Licenses: Expat
Build system: r
Synopsis: Markdown-Based Programmable Surveys Using 'Quarto' and 'shiny'
Description:

Generate programmable surveys using markdown and R code chunks. Surveys are composed of two files: a survey.qmd Quarto file defining the survey content (pages, questions, etc), and an app.R file defining a shiny app with global settings (libraries, database configuration, etc.) and server configuration options (e.g., conditional skipping / display, etc.). Survey data collected from respondents is stored in a PostgreSQL database. Features include controls for conditional skip logic (skip to a page based on an answer to a question), conditional display logic (display a question based on an answer to a question), a customizable progress bar, and a wide variety of question types, including multiple choice (single choice and multiple choices), select, text, numeric, multiple choice buttons, text area, and dates. Because the surveys render into a shiny app, designers can also leverage the reactive capabilities of shiny to create dynamic and interactive surveys.

r-smnlmec 1.0.2
Propagated dependencies: r-truncatednormal@2.3 r-tmvtnorm@1.7 r-stanheaders@2.32.10 r-rstan@2.32.7 r-numderiv@2016.8-1.1 r-mvtnorm@1.3-7 r-mnormt@2.1.2 r-mass@7.3-65 r-laplacesdemon@16.1.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/KelinZhong/SMNlmec
Licenses: GPL 3
Build system: r
Synopsis: Scale Mixture of Normal Distribution in Linear Mixed-Effects Model
Description:

Bayesian analysis of censored linear mixed-effects models that replace Gaussian assumptions with a flexible class of distributions, such as the scale mixture of normal family distributions, considering a damped exponential correlation structure which was employed to account for within-subject autocorrelation among irregularly observed measures. For more details, see Kelin Zhong, Fernanda L. Schumacher, Luis M. Castro, Victor H. Lachos (2025) <doi:10.1002/sim.10295>.

r-simts 0.2.4
Propagated dependencies: r-tidyr@1.3.2 r-scales@1.4.0 r-robcor@0.1-6.1 r-rlang@1.2.0 r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1 r-purrr@1.2.2 r-magrittr@2.0.5 r-dplyr@1.2.1 r-broom@1.0.13
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/SMAC-Group/simts
Licenses: AGPL 3 FSDG-compatible
Build system: r
Synopsis: Time Series Analysis Tools
Description:

This package provides a system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

r-sparsestep 1.0.1
Propagated dependencies: r-matrix@1.7-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/GjjvdBurg/SparseStep
Licenses: GPL 2+
Build system: r
Synopsis: SparseStep Regression
Description:

This package implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) <arXiv:1701.06967>. In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter.

r-swaprinc 1.0.1
Propagated dependencies: r-tidyselect@1.2.1 r-rlang@1.2.0 r-magrittr@2.0.5 r-lme4@2.0-1 r-gifi@1.0-0 r-dplyr@1.2.1 r-broom-mixed@0.2.9.7 r-broom@1.0.13
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mncube/swaprinc
Licenses: Expat
Build system: r
Synopsis: Swap Principal Components into Regression Models
Description:

Obtaining accurate and stable estimates of regression coefficients can be challenging when the suggested statistical model has issues related to multicollinearity, convergence, or overfitting. One solution is to use principal component analysis (PCA) results in the regression, as discussed in Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package streamlines comparisons between a raw regression model with the full set of raw independent variables and a principal component regression model where principal components are estimated on a subset of the independent variables, then swapped into the regression model in place of those variables. The swaprinc() function compares one raw regression model to one principal component regression model, while the compswap() function compares one raw regression model to many principal component regression models. Package functions include parameters to center, scale, and undo centering and scaling, as described by Harvey and Hansen (2022) <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>. Additionally, the package supports using Gifi methods to extract principal components from categorical variables, as outlined by Rossiter (2021) <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.

r-support-bws3 0.2-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=support.BWS3
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Case 3 Best-Worst Scaling
Description:

This package provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.

r-shiny-destroy 0.1.0
Propagated dependencies: r-shiny@1.13.0 r-rlang@1.2.0 r-purrr@1.2.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=shiny.destroy
Licenses: Expat
Build system: r
Synopsis: Create Destroyable Modules in 'Shiny'
Description:

Enables the complete removal of various Shiny components, such as inputs, outputs and modules. It also aids in the removal of observers that have been created in dynamically created modules.

r-secrettext 0.1.0
Propagated dependencies: r-tidyr@1.3.2 r-testthat@3.3.2 r-stringr@1.6.0 r-rlang@1.2.0 r-magrittr@2.0.5 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=secrettext
Licenses: Expat
Build system: r
Synopsis: Encrypt Text Using a Shifting Substitution Cipher
Description:

Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.

r-safejoin 0.2.0
Propagated dependencies: r-lifecycle@1.0.5 r-glue@1.8.1 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/SamEdwardes/safejoin
Licenses: Expat
Build system: r
Synopsis: Perform "Safe" Table Joins
Description:

The goal of safejoin is to guarantee that when performing joins extra rows are not added to your data. safejoin provides a wrapper around dplyr::left_join that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.

r-sample 1.0.1
Propagated dependencies: r-rmisc@1.5.1 r-rcolorbrewer@1.1-3 r-magrittr@2.0.5 r-ggplot2@4.0.3 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/yacinebenchehida/SAMPLE
Licenses: Expat
Build system: r
Synopsis: Estimate Sampling Effort for Species Occurrence Rates
Description:

Estimates the sampling effort needed to obtain stable species occurrence and prevalence rates from presence-absence data. The method repeatedly subsamples observations, evaluates changes in estimated rates, and reports the minimum stable sample size. For methodological details, see Bravo et al. (2025) <doi:10.1002/ece3.70998>.

r-steiniv 0.1-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SteinIV
Licenses: GPL 2+
Build system: r
Synopsis: Semi-Parametric Stein-Like Estimator with Instrumental Variables
Description:

Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004) <DOI:10.1198/016214504000000430>.

r-shuffle 1.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=shuffle
Licenses: GPL 2+
Build system: r
Synopsis: The Shuffle Estimator for Explainable Variance
Description:

Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.

r-subdetect 1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=subdetect
Licenses: GPL 3
Build system: r
Synopsis: Detect Subgroup with an Enhanced Treatment Effect
Description:

This package provides a test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.

r-sanvi 0.1.1
Propagated dependencies: r-scales@1.4.0 r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1 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-smartmap 0.2.0
Propagated dependencies: r-sf@1.1-1 r-magrittr@2.0.5 r-leaflet@2.2.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/s-fleck/smartmap
Licenses: Expat
Build system: r
Synopsis: Smartly Create Maps from R Objects
Description:

Preview spatial data as leaflet maps with minimal effort. smartmap is optimized for interactive use and distinguishes itself from similar packages because it does not need real spatial ('sp or sf') objects an input; instead, it tries to automatically coerce everything that looks like spatial data to sf objects or leaflet maps. It - for example - supports direct mapping of: a vector containing a single coordinate pair, a two column matrix, a data.frame with longitude and latitude columns, or the path or URL to a (possibly compressed) shapefile'.

r-scpairs 0.1.8
Propagated dependencies: r-tidyr@1.3.2 r-tidygraph@1.3.1 r-seuratobject@5.4.0 r-seurat@5.5.0 r-patchwork@1.3.2 r-matrix@1.7-5 r-igraph@2.3.1 r-ggrepel@0.9.8 r-ggraph@2.2.2 r-ggplot2@4.0.3 r-data-table@1.18.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/zhaoqing-wang/scPairs
Licenses: Expat
Build system: r
Synopsis: Identifying Synergistic Gene Pairs in Single-Cell and Spatial Transcriptomics
Description:

Discovers synergistic gene pairs in single-cell RNA-seq and spatial transcriptomics data. Unlike conventional pairwise co-expression analyses that rely on a single correlation metric, scPairs integrates 14 complementary metrics across five orthogonal evidence layers to compute a composite synergy score with optional permutation-based significance testing. The five evidence layers span cell-level co-expression (Pearson, Spearman, biweight midcorrelation, mutual information, ratio consistency), neighbourhood-aware smoothing (KNN-smoothed correlation, neighbourhood co-expression, cluster pseudo-bulk, cross-cell-type, neighbourhood synergy), prior biological knowledge (GO/KEGG co-annotation Jaccard, pathway bridge score), trans-cellular interaction, and spatial co-variation (Lee's L, co-location quotient). This multi-scale design enables researchers to move beyond simple co-expression towards a comprehensive characterisation of cooperative gene regulation at transcriptomic and spatial resolution. For more information, see the package documentation at <https://github.com/zhaoqing-wang/scPairs>.

r-snic 0.6.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/rolfsimoes/snic
Licenses: GPL 2+
Build system: r
Synopsis: Superpixel Segmentation with the Simple Non-Iterative Clustering Algorithm
Description:

This package implements the Simple Non-Iterative Clustering algorithm for superpixel segmentation of multi-band images, as introduced by Achanta and Susstrunk (2017) <doi:10.1109/CVPR.2017.520>. Supports both standard image arrays and geospatial raster objects, with a design that can be extended to other spatial data frameworks. The algorithm groups adjacent pixels into compact, coherent regions based on spectral similarity and spatial proximity. A high-performance implementation supports images with arbitrary spectral bands.

r-spetestnp 1.1.0
Propagated dependencies: r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/HippolyteBoucher/SpeTestNP
Licenses: GPL 2
Build system: r
Synopsis: Non-Parametric Tests of Parametric Specifications
Description:

This package performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. Allows parallel computing to quickly compute p-values based on the bootstrap. Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>, J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>, P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>, P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>, P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>, J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>, C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>, J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002> and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.

Total packages: 73977