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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-simsst 0.0.5.2
Propagated dependencies: r-mass@7.3-65 r-gamlss-dist@6.1-1 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=SimSST
Licenses: GPL 3
Build system: r
Synopsis: Simulated Stop Signal Task Data
Description:

Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to BEESTS software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>.

r-smle 2.2-3
Propagated dependencies: r-mvnfast@0.2.8 r-matrixcalc@1.0-6 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SMLE
Licenses: GPL 3
Build system: r
Synopsis: Joint Feature Screening via Sparse MLE
Description:

Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)<doi:10.1080/01621459.2013.879531> proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion. Zang, Xu, and Burkett (2025)<doi:10.18637/jss.v115.i08>.

r-secutrialr 1.3.3
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-readxl@1.4.5 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-haven@2.5.5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/SwissClinicalTrialOrganisation/secuTrialR
Licenses: Expat
Build system: r
Synopsis: Handling of Data from the Clinical Data Management System 'secuTrial'
Description:

Seamless and standardized interaction with data exported from the clinical data management system (CDMS) secuTrial'<https://www.secutrial.com>. The primary data export the package works with is a standard non-rectangular export.

r-sperich 1.5-9
Propagated dependencies: r-sp@2.2-0 r-raster@3.6-32 r-foreach@1.5.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sperich
Licenses: GPL 2+
Build system: r
Synopsis: Auxiliary Functions to Estimate Centers of Biodiversity
Description:

This package provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

r-scalespikeslab 1.0
Propagated dependencies: r-truncatednormal@2.3 r-rcppeigen@0.3.4.0.2 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=ScaleSpikeSlab
Licenses: GPL 2+
Build system: r
Synopsis: Scalable Spike-and-Slab
Description:

This package provides a scalable Gibbs sampling implementation for high dimensional Bayesian regression with the continuous spike-and-slab prior. Niloy Biswas, Lester Mackey and Xiao-Li Meng, "Scalable Spike-and-Slab" (2022) <arXiv:2204.01668>.

r-salso 0.3.57
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dbdahl/salso
Licenses: Expat ASL 2.0
Build system: r
Synopsis: Search Algorithms and Loss Functions for Bayesian Clustering
Description:

The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.

r-shapper 0.1.3
Propagated dependencies: r-reticulate@1.44.1 r-ggplot2@4.0.1 r-dalex@2.5.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/ModelOriented/shapper
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Wrapper of Python Library 'shap'
Description:

This package provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package shapper is a port of the Python library shap'.

r-sleekts 1.0.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sleekts
Licenses: GPL 3
Build system: r
Synopsis: 4253H, Twice Smoothing
Description:

Compute Time series Resistant Smooth 4253H, twice smoothing method.

r-symsem 0.4
Dependencies: python@3.11.14
Propagated dependencies: r-openmx@2.22.10 r-metasem@1.5.0 r-caracas@2.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mikewlcheung/symsem
Licenses: GPL 2+
Build system: r
Synopsis: Symbolic Computation for Structural Equation Models
Description:

This package provides a collection of functions for symbolic computation using the caracas package for structural equation models and other statistical analyses. Among its features is the ability to calculate the model-implied covariance (and correlation) matrix and the sampling covariance matrix of variable functions using the delta method.

r-scirmdtheme 0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/oobianom/sciRmdTheme
Licenses: Expat
Build system: r
Synopsis: Upgraded 'Rmarkdown' Themes for Scientific Writing
Description:

This package provides a set of Rmarkdown themes for creating scientific and professional documents. Simple interface with features to ease navigation across the page and sub-pages.

r-seqhmm 2.1.0
Propagated dependencies: r-traminer@2.2-13 r-rlang@1.1.6 r-rcpphungarian@0.3 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-progressr@0.18.0 r-patchwork@1.3.2 r-numderiv@2016.8-1.1 r-nloptr@2.2.1 r-matrix@1.7-4 r-lhs@1.2.0 r-igraph@2.2.1 r-gridbase@0.4-7 r-ggseqplot@0.8.9 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-data-table@1.17.8 r-collapse@2.1.5 r-cli@3.6.5 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=seqHMM
Licenses: GPL 2+
Build system: r
Synopsis: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series
Description:

Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, <doi:10.18637/jss.v088.i03>). For methodology behind the NHMMs, see Helske (2025, <doi:10.48550/arXiv.2503.16014>).

r-swaglm 0.0.1
Propagated dependencies: r-scales@1.4.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-progress@1.2.3 r-plyr@1.8.9 r-igraph@2.2.1 r-gdata@3.0.1 r-fields@17.1 r-fastglm@0.0.3 r-desctools@0.99.60
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=swaglm
Licenses: AGPL 3
Build system: r
Synopsis: Fast Sparse Wrapper Algorithm for Generalized Linear Models and Testing Procedures for Network of Highly Predictive Variables
Description:

This package provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.

r-simbiid 0.2.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rcppxptrutils@0.1.3 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-mvtnorm@1.3-3 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/tjmckinley/SimBIID
Licenses: GPL 3+
Build system: r
Synopsis: Simulation-Based Inference Methods for Infectious Disease Models
Description:

This package provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC) algorithm (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). Also provides functions to plot and summarise the outputs.

r-shinyloadtest 1.2.1
Propagated dependencies: r-xml2@1.5.0 r-websocket@1.4.4 r-vroom@1.6.6 r-svglite@2.2.2 r-stringr@1.6.0 r-scales@1.4.0 r-rlang@1.1.6 r-r6@2.6.1 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-httpuv@1.6.16 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://rstudio.github.io/shinyloadtest/
Licenses: GPL 3
Build system: r
Synopsis: Load Test Shiny Applications
Description:

Assesses the number of concurrent users shiny applications are capable of supporting, and for directing application changes in order to support a higher number of users. Provides facilities for recording shiny application sessions, playing recorded sessions against a target server at load, and analyzing the resulting metrics.

r-subts 1.0
Propagated dependencies: r-tweedie@2.3.5 r-gsl@2.1-9 r-copula@1.1-6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SubTS
Licenses: GPL 3+
Build system: r
Synopsis: Positive Tempered Stable Distributions and Related Subordinators
Description:

This package contains methods for the simulation of positive tempered stable distributions and related subordinators. Including classical tempered stable, rapidly deceasing tempered stable, truncated stable, truncated tempered stable, generalized Dickman, truncated gamma, generalized gamma, and p-gamma. For details, see Dassios et al (2019) <doi:10.1017/jpr.2019.6>, Dassios et al (2020) <doi:10.1145/3368088>, Grabchak (2021) <doi:10.1016/j.spl.2020.109015>.

r-singlecasees 0.7.3
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-rlang@1.1.6 r-purrr@1.2.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://jepusto.github.io/SingleCaseES/
Licenses: GPL 3
Build system: r
Synopsis: Calculator for Single-Case Effect Sizes
Description:

This package provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions.

r-sam 1.1.3
Propagated dependencies: r-rcppeigen@0.3.4.0.2 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=SAM
Licenses: GPL 2
Build system: r
Synopsis: Sparse Additive Modelling
Description:

Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks.

r-spartaas 1.2.4
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-shinywidgets@0.9.0 r-shinythemes@1.2.0 r-shinyjs@2.1.0 r-shinyjqui@0.4.1 r-shinydashboard@0.7.3 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-scatterd3@1.0.1 r-scales@1.4.0 r-rstudioapi@0.17.1 r-plotly@4.11.0 r-nor1mix@1.3-3 r-mass@7.3-65 r-lmtest@0.9-40 r-leaflet@2.2.3 r-ks@1.15.1 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-ggplot2@4.0.1 r-ggdendro@0.2.0 r-fpc@2.2-13 r-foreign@0.8-90 r-fastcluster@1.3.0 r-factominer@2.12 r-explor@0.3.10 r-dplyr@1.1.4 r-colorspace@2.1-2 r-cluster@2.1.8.1 r-ape@5.8-1 r-ade4@1.7-23
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://spartaas.gitpages.huma-num.fr/r-package/
Licenses: GPL 2+
Build system: r
Synopsis: Statistical Pattern Recognition and daTing using Archaeological Artefacts assemblageS
Description:

Statistical pattern recognition and dating using archaeological artefacts assemblages. Package of statistical tools for archaeology. hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4). mapclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>. seriograph(): Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>. cerardat(): Bellanger Lise, Husi Philippe (2012) <doi:10.1016/j.jas.2011.06.031>.

r-scdha 1.2.3
Propagated dependencies: r-uwot@0.2.4 r-torch@0.16.3 r-rhpcblasctl@0.23-42 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcppannoy@0.0.22 r-rcpp@1.1.0 r-matrixstats@1.5.0 r-matrix@1.7-4 r-igraph@2.2.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-coro@1.1.0 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/duct317/scDHA
Licenses: GPL 3
Build system: r
Synopsis: Single-Cell Decomposition using Hierarchical Autoencoder
Description:

This package provides a fast and accurate pipeline for single-cell analyses. The scDHA software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).

r-statlingua 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/bgreenwell/statlingua
Licenses: GPL 2+
Build system: r
Synopsis: Explain Statistical Output with Large Language Models
Description:

Transform complex statistical output into straightforward, understandable, and context-aware natural language descriptions using Large Language Models (LLMs), making complex analyses more accessible to individuals with varying statistical expertise. It relies on the ellmer package to interface with LLM providers including OpenAI <https://openai.com/>, Google AI Studio <https://aistudio.google.com/>, and Anthropic <https://www.anthropic.com/> (API keys are required and managed via ellmer').

r-sequentialdesign 1.0
Propagated dependencies: r-sequential@4.5.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SequentialDesign
Licenses: GPL 2
Build system: r
Synopsis: Observational Database Study Planning using Exact Sequential Analysis for Poisson and Binomial Data
Description:

This package provides functions to be used in conjunction with the Sequential package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the Sequential package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the Sequential package on these populations, which allows for the exploration of outcome misclassification in data.

r-stableestim 2.4
Propagated dependencies: r-stabledist@0.7-2 r-rdpack@2.6.4 r-numderiv@2016.8-1.1 r-mass@7.3-65 r-fbasics@4041.97
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://geobosh.github.io/StableEstim/
Licenses: GPL 2+
Build system: r
Synopsis: Estimate the Four Parameters of Stable Laws using Different Methods
Description:

Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided.

r-simmetric 0.1.1
Propagated dependencies: r-dplyr@1.1.4 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=simMetric
Licenses: Expat
Build system: r
Synopsis: Metrics (with Uncertainty) for Simulation Studies that Evaluate Statistical Methods
Description:

Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.

r-samplingbigdata 1.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/jlisic/SamplingBigData
Licenses: GPL 2+
Build system: r
Synopsis: Sampling Methods for Big Data
Description:

Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.

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