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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-scorepeak 0.1.2
Propagated dependencies: r-rcpp@1.1.0 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/ShotaOchi/scorepeak
Licenses: GPL 3
Build system: r
Synopsis: Peak Functions for Peak Detection in Univariate Time Series
Description:

This package provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

r-seqicp 1.1
Propagated dependencies: r-mgcv@1.9-4 r-dhsic@2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=seqICP
Licenses: GPL 3
Build system: r
Synopsis: Sequential Invariant Causal Prediction
Description:

This package contains an implementation of invariant causal prediction for sequential data. The main function in the package is seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines seqICP.s and seqICPnl.s corresponding to the respective main methods.

r-statiovar 0.1.3
Propagated dependencies: r-rlang@1.1.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/g-corbelli/statioVAR
Licenses: GPL 3
Build system: r
Synopsis: Trend Removal for Vector Autoregressive Workflows
Description:

Detrending multivariate time-series to approximate stationarity when dealing with intensive longitudinal data, prior to Vector Autoregressive (VAR) or multilevel-VAR estimation. Classical VAR assumes weak stationarity (constant first two moments), and deterministic trends inflate spurious autocorrelation, biasing Granger-causality and impulse-response analyses. All functions operate on raw panel data and write detrended columns back to the data set, but differ in the level at which the trend is estimated. See, for instance, Wang & Maxwell (2015) <doi:10.1037/met0000030>; Burger et al. (2022) <doi:10.4324/9781003111238-13>; Epskamp et al. (2018) <doi:10.1177/2167702617744325>.

r-ssabss 0.1.1
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-tsbss@1.0.0 r-jade@2.0-4 r-ictest@0.3-6 r-ggplot2@4.0.1 r-bssprep@0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ssaBSS
Licenses: GPL 2+
Build system: r
Synopsis: Stationary Subspace Analysis
Description:

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

r-spader 0.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SpadeR
Licenses: GPL 3+
Build system: r
Synopsis: Species-Richness Prediction and Diversity Estimation with R
Description:

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

r-sspm 1.1.0
Propagated dependencies: r-units@1.0-0 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-sf@1.0-23 r-rlang@1.1.6 r-purrr@1.2.0 r-mgcv@1.9-4 r-magrittr@2.0.4 r-dplyr@1.1.4 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://pedersen-fisheries-lab.github.io/sspm/
Licenses: Expat
Build system: r
Synopsis: Spatial Surplus Production Model Framework for Northern Shrimp Populations
Description:

Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).

r-sdmodels 2.0.2
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-rdpack@2.6.4 r-progressr@0.18.0 r-igraph@2.2.1 r-grplasso@0.4-7 r-gridextra@2.3 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-future@1.68.0 r-fda@6.3.0 r-diagrammer@1.0.11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://www.markus-ulmer.ch/SDModels/
Licenses: GPL 3
Build system: r
Synopsis: Spectrally Deconfounded Models
Description:

Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ä evid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ä evid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. SDModels provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.1080/10618600.2025.2569602>).

r-schumaker 1.2.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=schumaker
Licenses: Expat
Build system: r
Synopsis: Schumaker Shape-Preserving Spline
Description:

This is a shape preserving spline <doi:10.1137/0720057> which is guaranteed to be monotonic and concave or convex if the data is monotonic and concave or convex. It does not use any optimisation and is therefore quick and smoothly converges to a fixed point in economic dynamics problems including value function iteration. It also automatically gives the first two derivatives of the spline and options for determining behaviour when evaluated outside the interpolation domain.

r-scenes 0.1.0
Propagated dependencies: r-shiny@1.11.1 r-rlang@1.1.6 r-purrr@1.2.0 r-glue@1.8.0 r-cookies@0.2.3 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/r4ds/scenes
Licenses: Expat
Build system: r
Synopsis: Switch Between Alternative 'shiny' UIs
Description:

Sometimes it is useful to serve up alternative shiny UIs depending on information passed in the request object, such as the value of a cookie or a query parameter. This packages facilitates such switches.

r-sketching 0.1.2
Propagated dependencies: r-rcpp@1.1.0 r-phangorn@2.12.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/sokbae/sketching/
Licenses: GPL 3
Build system: r
Synopsis: Sketching of Data via Random Subspace Embeddings
Description:

Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45â 80.

r-simevent 0.1.0
Propagated dependencies: r-survival@3.8-3 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/miclukacova/simevent
Licenses: Expat
Build system: r
Synopsis: Simulation and Analysis of Event History Data
Description:

Simulate event history data from a framework where treatment decisions and disease progression are represented as counting process. The user can specify number of events and parameters of intensities thereby creating a flexible simulation framework.

r-smarter 1.0.1
Propagated dependencies: r-usethis@3.2.1 r-rmarkdown@2.30 r-rcurl@1.98-1.17 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-gplots@3.2.0 r-devtools@2.4.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=smarter
Licenses: GPL 3+
Build system: r
Synopsis: Collection of Modified R Functions to Make Basic Coding More Convenient
Description:

This package provides a collection of recycled and modified R functions to aid in file manipulation, data exploration, wrangling, optimization, and object manipulation. Other functions aid in convenient data visualization, loop progression, software packaging, and installation.

r-starstileserver 0.1.1
Propagated dependencies: r-units@1.0-0 r-stars@0.6-8 r-sf@1.0-23 r-rlang@1.1.6 r-r6@2.6.1 r-png@0.1-8 r-plumber@1.3.0 r-leaflet@2.2.3 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://bartk.gitlab.io/starsTileServer
Licenses: GPL 3
Build system: r
Synopsis: Dynamic Tile Server for R
Description:

Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.

r-semboottools 0.1.1
Propagated dependencies: r-psych@2.5.6 r-lavaan-printer@0.1.0 r-lavaan@0.6-20 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://Yangzhen1999.github.io/semboottools/
Licenses: GPL 3+
Build system: r
Synopsis: Bootstrapping Helpers for Structural Equation Modelling
Description:

This package provides a collection of helper functions for forming bootstrapping confidence intervals and examining bootstrap estimates in structural equation modelling. Currently supports models fitted by the lavaan package by Rosseel (2012) <doi: 10.18637/jss.v048.i02>.

r-speechbr 2.0.0
Propagated dependencies: r-xml2@1.5.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rvest@1.0.5 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-janitor@2.2.1 r-httr@1.4.7 r-httptest@4.2.3 r-glue@1.8.0 r-dplyr@1.1.4 r-abjutils@0.3.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dcardosos/speechbr
Licenses: Expat
Build system: r
Synopsis: Access the Speechs and Speaker's Informations of House of Representatives of Brazil
Description:

Scrap speech text and speaker informations of speeches of House of Representatives of Brazil, and transform in a cleaned tibble.

r-statcodelists 0.9.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://statcodelists.dataobservatory.eu
Licenses: CC0
Build system: r
Synopsis: Use Standardized Statistical Codelists
Description:

The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

r-sdf-test 0.0.1.0
Propagated dependencies: r-dtt@0.1-2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sdf.test
Licenses: GPL 3+
Build system: r
Synopsis: Nonparametric Two Sample Test for Equality of Spectral Densities
Description:

Nonparametric method for testing the equality of the spectral densities of two time series of possibly different lengths. The time series are preprocessed with the discrete cosine transform and the variance stabilising transform to obtain an approximate Gaussian regression setting for the log-spectral density function. The test statistic is based on the squared L2 norm of the difference between the estimated log-spectral densities. The test returns the result, the statistic value, and the p-value. It also provides the estimated empirical quantile and null distribution under the hypothesis of equal spectral densities. An example using EEG data is included. For details see Nadin, Krivobokova, Enikeeva (2026), <doi:10.48550/arXiv.2602.10774>.

r-states 0.3.3
Propagated dependencies: r-rlang@1.1.6 r-lifecycle@1.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/andybega/states
Licenses: Expat
Build system: r
Synopsis: Create Panels of Independent States
Description:

Create panel data consisting of independent states from 1816 to the present. The package includes the Gleditsch & Ward (G&W) and Correlates of War (COW) lists of independent states, as well as helper functions for working with state panel data and standardizing other data sources to create country-year/month/etc. data.

r-selectspm 0.7
Propagated dependencies: r-spatstat-random@3.4-3 r-spatstat-model@3.5-0 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-spatstat@3.4-1 r-ecespa@1.1-18
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=selectspm
Licenses: GPL 2+
Build system: r
Synopsis: Select Point Pattern Models Based on Minimum Contrast, AIC and Goodness of Fit
Description:

Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.

r-slhd 2.1-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SLHD
Licenses: LGPL 2.1
Build system: r
Synopsis: Maximin-Distance (Sliced) Latin Hypercube Designs
Description:

Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".

r-scuba 1.11-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scuba
Licenses: GPL 2+
Build system: r
Synopsis: Diving Calculations and Decompression Models
Description:

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

r-survivalrec 1.1
Propagated dependencies: r-survival@3.8-3 r-kernsmooth@2.23-26
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=survivalREC
Licenses: GPL 3
Build system: r
Synopsis: Nonparametric Estimation of the Distribution of Gap Times for Recurrent Events
Description:

This package provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) <doi:10.1093/biomet/86.1.59> and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published.

r-sparseflmm 0.4.2
Propagated dependencies: r-refund@0.1-38 r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sparseFLMM
Licenses: GPL 2
Build system: r
Synopsis: Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data
Description:

Estimation of functional linear mixed models for irregularly or sparsely sampled data based on functional principal component analysis.

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>.

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