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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-sft 2.4
Propagated dependencies: r-suppdists@1.1-9.9 r-fda@6.3.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sft
Licenses: GPL 2+
Build system: r
Synopsis: Functions for Systems Factorial Technology Analysis of Data
Description:

This package provides a series of tools for analyzing Systems Factorial Technology data. This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions. Houpt, Blaha, McIntire, Havig, and Townsend (2013) <doi:10.3758/s13428-013-0377-3> provide a basic introduction to Systems Factorial Technology along with examples using the sft R package.

r-serrsbayes 0.5-0
Propagated dependencies: r-truncnorm@1.0-9 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mooresm/serrsBayes
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Bayesian Modelling of Raman Spectroscopy
Description:

Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.

r-smnlmec 1.0.0
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@1.3-3 r-mnormt@2.1.1 r-mass@7.3-65 r-laplacesdemon@16.1.6
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-semaphore 1.2.0
Propagated dependencies: r-rcpp@1.1.0 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cmmr.github.io/semaphore/
Licenses: Expat
Build system: r
Synopsis: Shared Memory Atomic Operations
Description:

This package implements named semaphores from the boost C++ library <https://www.boost.org/> for interprocess communication. Multiple R sessions on the same host can block (with optional timeout) on a semaphore until it becomes positive, then atomically decrement it and unblock. Any session can increment the semaphore.

r-sistmr 0.1.1
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ggbeeswarm@0.7.2 r-dplyr@1.1.4 r-blandaltmanleh@0.3.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sistmr
Licenses: Expat
Build system: r
Synopsis: Collection of Utility Function from the Inserm/Inria SISTM Team
Description:

This package provides functions common to members of the SISTM team.

r-sqrl 1.0.2
Propagated dependencies: r-rodbc@1.3-26.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SQRL
Licenses: GPL 3
Build system: r
Synopsis: Enhances Interaction with 'ODBC' Databases
Description:

This package provides simple and powerful interfaces that facilitate interaction with ODBC data sources. Each data source gets its own unique and dedicated interface, wrapped around RODBC'. Communication settings are remembered between queries, and are managed silently in the background. The interfaces support multi-statement SQL scripts, which can be parameterised via metaprogramming structures and embedded R expressions.

r-survstan 0.0.7.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-survival@3.8-3 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rlang@1.1.6 r-rdpack@2.6.4 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-purrr@1.2.0 r-mass@7.3-65 r-gridextra@2.3 r-ggplot2@4.0.1 r-generics@0.1.4 r-future@1.68.0 r-foreach@1.5.2 r-extradistr@1.10.0 r-dplyr@1.1.4 r-dofuture@1.1.2 r-broom@1.0.10 r-bh@1.87.0-1 r-actuar@3.3-6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/fndemarqui/survstan
Licenses: Expat
Build system: r
Synopsis: Fitting Survival Regression Models via 'Stan'
Description:

Parametric survival regression models under the maximum likelihood approach via Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.

r-siber 2.1.10
Dependencies: jags@4.3.1
Propagated dependencies: r-tidyr@1.3.1 r-spatstat-utils@3.2-0 r-rjags@4-17 r-mnormt@2.1.1 r-magrittr@2.0.4 r-hdrcde@3.4 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=SIBER
Licenses: GPL 2+
Build system: r
Synopsis: Stable Isotope Bayesian Ellipses in R
Description:

Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche.

r-semiparmf 1.0.0
Propagated dependencies: r-spdep@1.4-1 r-sf@1.0-23
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/jzeuzs/SemiparMF
Licenses: Expat
Build system: r
Synopsis: Semiparametric Spatiotemporal Model with Mixed Frequencies
Description:

Fits a semiparametric spatiotemporal model for data with mixed frequencies, specifically where the response variable is observed at a lower frequency than some covariates. The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects, and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022) <https://scienggj.org/2022/SciEnggJ%202022-vol15-no02-p90-107-Malabanan%20et%20al.pdf>.

r-sdbuildr 1.0.8
Propagated dependencies: r-xml2@1.5.0 r-stringr@1.6.0 r-rvest@1.0.5 r-rlang@1.1.6 r-purrr@1.2.0 r-plotly@4.11.0 r-magrittr@2.0.4 r-juliaconnector@1.1.5 r-jsonlite@2.0.0 r-igraph@2.2.1 r-dplyr@1.1.4 r-diagrammer@1.0.11 r-desolve@1.40 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://kcevers.github.io/sdbuildR/
Licenses: GPL 3+
Build system: r
Synopsis: Easily Build, Simulate, and Visualise Stock-and-Flow Models
Description:

Stock-and-flow models are a computational method from the field of system dynamics. They represent how systems change over time and are mathematically equivalent to ordinary differential equations. sdbuildR (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. sdbuildR simulates models in R and Julia', where Julia offers unit support and large-scale ensemble simulations. Additionally, sdbuildR can import models created in Insight Maker (<https://insightmaker.com/>).

r-scepterbinary 0.1-1
Propagated dependencies: r-scepter@0.2-4 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=SCEPtERbinary
Licenses: GPL 2+
Build system: r
Synopsis: Stellar CharactEristics Pisa Estimation gRid for Binary Systems
Description:

SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.

r-semidist 0.1.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-purrr@1.2.0 r-furrr@0.3.1 r-fnn@1.1.4.1 r-energy@1.7-12
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/wzhong41/semidist
Licenses: Expat
Build system: r
Synopsis: Measure Dependence Between Categorical and Continuous Variables
Description:

Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable. Test of independence and feature screening for classification problems can be implemented via the two dependence measures. For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>; Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>; Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.

r-spatialdownscaling 0.1.2
Dependencies: python@3.11.14
Propagated dependencies: r-tensorflow@2.20.0 r-rdpack@2.6.4 r-raster@3.6-32 r-magrittr@2.0.4 r-keras3@1.5.0 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=SpatialDownscaling
Licenses: GPL 3
Build system: r
Synopsis: Methods for Spatial Downscaling Using Deep Learning
Description:

The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 <doi:10.1029/2020WR029308>) and UNet (Ronneberger et al., 2015 <doi:10.1007/978-3-319-24574-4_28>), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 <doi:10.1023/B:CLIM.0000013685.99609.9e>). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) <doi:10.48550/arXiv.2512.13753>.

r-sensmediation 0.3.1
Propagated dependencies: r-mvtnorm@1.3-3 r-maxlik@1.5-2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sensmediation
Licenses: GPL 2
Build system: r
Synopsis: Parametric Estimation and Sensitivity Analysis of Direct and Indirect Effects
Description:

We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.

r-slos 1.0.1
Propagated dependencies: r-ranger@0.17.0 r-mlmetrics@1.1.3 r-magrittr@2.0.4 r-httr@1.4.7 r-ggplot2@4.0.1 r-ems@1.3.11 r-dplyr@1.1.4 r-caretensemble@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SLOS
Licenses: Expat
Build system: r
Synopsis: ICU Length of Stay Prediction and Efficiency Evaluation
Description:

This package provides tools for predicting ICU length of stay and assessing ICU efficiency. It is based on the methodologies proposed by Peres et al. (2022, 2023), which utilize data-driven approaches for modeling and validation, offering insights into ICU performance and patient outcomes. References: Peres et al. (2022)<https://pubmed.ncbi.nlm.nih.gov/35988701/>, Peres et al. (2023)<https://pubmed.ncbi.nlm.nih.gov/37922007/>. More information: <https://github.com/igor-peres/ICU-Length-of-Stay-Prediction>.

r-spd 2.0-1
Propagated dependencies: r-kernsmooth@2.23-26
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://www.unstarched.net
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Semi Parametric Distribution
Description:

The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.

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-sears 0.1.0
Propagated dependencies: r-boin@2.7.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SEARS
Licenses: GPL 2
Build system: r
Synopsis: Seamless Dose Escalation/Expansion with Adaptive Randomization Scheme
Description:

This package provides a seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy.

r-sr 0.1.0
Propagated dependencies: r-vdiffr@1.0.8 r-rann@2.6.2 r-progress@1.2.3 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://smoothregression.com
Licenses: GPL 3+
Build system: r
Synopsis: Smooth Regression - The Gamma Test and Tools
Description:

Finds causal connections in precision data, finds lags and embeddings in time series, guides training of neural networks and other smooth models, evaluates their performance, gives a mathematically grounded answer to the over-training problem. Smooth regression is based on the Gamma test, which measures smoothness in a multivariate relationship. Causal relations are smooth, noise is not. sr includes the Gamma test and search techniques that use it. References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>, AJ Jones (2004) <doi:10.1007/s10287-003-0006-1>.

r-scgwr 0.1.2-21
Propagated dependencies: r-spdata@2.3.4 r-sp@2.2-0 r-optimparallel@1.0-2 r-fnn@1.1.4.1 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=scgwr
Licenses: GPL 2+
Build system: r
Synopsis: Scalable Geographically Weighted Regression
Description:

Fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.

r-swaprinc 1.0.1
Propagated dependencies: r-tidyselect@1.2.1 r-rlang@1.1.6 r-magrittr@2.0.4 r-lme4@1.1-37 r-gifi@1.0-0 r-dplyr@1.1.4 r-broom-mixed@0.2.9.6 r-broom@1.0.10
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-shapechange 1.5
Propagated dependencies: r-quadprog@1.5-8 r-coneproj@1.23
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ShapeChange
Licenses: GPL 2+
Build system: r
Synopsis: Change-Point Estimation using Shape-Restricted Splines
Description:

In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.

r-sesraster 0.7.1
Propagated dependencies: r-terra@1.8-86 r-rlang@1.1.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://CRAN.R-project.org/package=SESraster
Licenses: GPL 3+
Build system: r
Synopsis: Raster Randomization for Null Hypothesis Testing
Description:

Randomization of presence/absence species distribution raster data with or without including spatial structure for calculating standardized effect sizes and testing null hypothesis. The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster data.

r-sccs 1.7
Propagated dependencies: r-survival@3.8-3 r-r-methodss3@1.8.2 r-gnm@1.1-5 r-fda@6.3.0 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SCCS
Licenses: GPL 2+
Build system: r
Synopsis: The Self-Controlled Case Series Method
Description:

Various self-controlled case series models used to investigate associations between time-varying exposures such as vaccines or other drugs or non drug exposures and an adverse event can be fitted. Detailed information on the self-controlled case series method and its extensions with more examples can be found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y. (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and <https://sccs-studies.info/index.html>.

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