_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-scoreeb 0.1.1
Propagated dependencies: 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=ScoreEB
Licenses: GPL 3
Build system: r
Synopsis: Score Test Integrated with Empirical Bayes for Association Study
Description:

Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide.

r-sfislands 1.1.2
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-spdep@1.4-1 r-sf@1.0-23 r-purrr@1.2.0 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-broom-mixed@0.2.9.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/horankev/sfislands
Licenses: Expat
Build system: r
Synopsis: Streamlines the Process of Fitting Areal Spatial Models
Description:

Helpers for addressing the issue of disconnected spatial units. It allows for convenient adding and removal of neighbourhood connectivity between areal units prior to modelling, with the visual aid of maps. Post-modelling, it reduces the human workload for extracting, tidying and mapping predictions from areal models.

r-saehb 0.2.3
Dependencies: jags@4.3.1
Propagated dependencies: r-stringr@1.6.0 r-rjags@4-17 r-coda@0.19-4.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/zazaperwira/saeHB
Licenses: GPL 3
Build system: r
Synopsis: Small Area Estimation using Hierarchical Bayesian Method
Description:

This package provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The rjags package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

r-shinychakraslider 0.1.0
Propagated dependencies: r-shiny@1.11.1 r-reactr@0.6.1 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/stla/shinyChakraSlider
Licenses: GPL 3
Build system: r
Synopsis: Combined Slider and Numeric Input for 'Shiny'
Description:

This package provides a combined slider and numeric input for usage in a Shiny app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).

r-sanitizers 0.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/eddelbuettel/sanitizers
Licenses: GPL 2+
Build system: r
Synopsis: C/C++ Source Code to Trigger Address and Undefined Behaviour Sanitizers
Description:

Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". . This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). . As an alternative, pre-built Docker containers such as the Rocker container r-devel-san or the multi-purpose container r-debug can be used. . This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. . The code samples are based on the examples from the Address Sanitizer Wiki at <https://github.com/google/sanitizers/wiki>.

r-snem 0.1.1
Propagated dependencies: 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=snem
Licenses: GPL 2+
Build system: r
Synopsis: EM Algorithm for Multivariate Skew-Normal Distribution with Overparametrization
Description:

Efficient estimation of multivariate skew-normal distribution in closed form.

r-saascnv 0.3.4
Propagated dependencies: r-rann@2.6.2 r-dnacopy@1.84.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://zhangz05.u.hpc.mssm.edu/saasCNV/
Licenses: GPL 2+
Build system: r
Synopsis: Somatic Copy Number Alteration Analysis Using Sequencing and SNP Array Data
Description:

Perform joint segmentation on two signal dimensions derived from total read depth (intensity) and allele specific read depth (intensity) for whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.

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-sylly 0.1-7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://reaktanz.de/?c=hacking&s=sylly
Licenses: GPL 3+
Build system: r
Synopsis: Hyphenation and Syllable Counting for Text Analysis
Description:

This package provides the hyphenation algorithm used for TeX'/'LaTeX and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the koRpus package, but later released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).

r-sdaa 0.1-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SDaA
Licenses: GPL 3
Build system: r
Synopsis: Sampling: Design and Analysis
Description:

This package provides functions and Datasets from Lohr, S. (1999), Sampling: Design and Analysis, Duxbury.

r-somenv 1.1.2
Propagated dependencies: r-shinycustomloader@0.9.0 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-rlist@0.4.6.2 r-plyr@1.8.9 r-openair@2.19.0 r-kohonen@3.0.12 r-dplyr@1.1.4 r-colourpicker@1.3.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/SomEnv/somenv
Licenses: GPL 3
Build system: r
Synopsis: SOM Algorithm for the Analysis of Multivariate Environmental Data
Description:

Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on kohonen and openair packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf> heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.

r-stepbeta 2.1.0
Propagated dependencies: r-mass@7.3-65 r-glue@1.8.0 r-combinat@0.0-8 r-betareg@3.2-4 r-aod@1.3.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=StepBeta
Licenses: GPL 3
Build system: r
Synopsis: Stepwise Procedure for Beta, Beta-Binomial and Negative Binomial Regression Models
Description:

Starting from a Regression Model, it provides a stepwise procedure to select the linear predictor.

r-surrogateseq 1.1
Propagated dependencies: r-mass@7.3-65 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=SurrogateSeq
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Group Sequential Testing of a Treatment Effect Using a Surrogate Marker
Description:

This package provides functions to implement group sequential procedures that allow for early stopping to declare efficacy using a surrogate marker and the possibility of futility stopping. More details are available in: Parast, L. and Bartroff, J (2024) <doi:10.1093/biomtc/ujae108>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogateseq>. A Shiny App implementing the methods can be found at <https://parastlab.shinyapps.io/SurrogateSeqApp/>.

r-scar 0.2-2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scar
Licenses: GPL 2+
Build system: r
Synopsis: Shape-Constrained Additive Regression: a Maximum Likelihood Approach
Description:

Computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing. For details, see Chen and Samworth (2016) <doi:10.1111/rssb.12137>.

r-surrosurvroc 0.1.0
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=surrosurvROC
Licenses: GPL 2+
Build system: r
Synopsis: Surrogate Survival ROC
Description:

Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.

r-shrink 1.2.3
Propagated dependencies: r-survival@3.8-3 r-rms@8.1-0 r-mfp@1.5.5.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/biometrician/shrink
Licenses: GPL 3
Build system: r
Synopsis: Global, Parameterwise and Joint Shrinkage Factor Estimation
Description:

The predictive value of a statistical model can often be improved by applying shrinkage methods. This can be achieved, e.g., by regularized regression or empirical Bayes approaches. Various types of shrinkage factors can also be estimated after a maximum likelihood. While global shrinkage modifies all regression coefficients by the same factor, parameterwise shrinkage factors differ between regression coefficients. With variables which are either highly correlated or associated with regard to contents, such as several columns of a design matrix describing a nonlinear effect, parameterwise shrinkage factors are not interpretable and a compromise between global and parameterwise shrinkage, termed joint shrinkage', is a useful extension. A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA residuals can be applied. Global, parameterwise and joint shrinkage for models fitted by lm(), glm(), coxph(), or mfp() is available.

r-slideview 0.2.1
Propagated dependencies: r-viridislite@0.4.2 r-terra@1.8-86 r-raster@3.6-32 r-lattice@0.22-7 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://r-spatial.github.io/slideview/
Licenses: Expat
Build system: r
Synopsis: Compare Raster Images Side by Side with a Slider
Description:

Create a side-by-side view of raster(image)s with an interactive slider to switch between regions of the images. This can be especially useful for image comparison of the same region at different time stamps.

r-silm 1.0.0
Propagated dependencies: r-sis@0.8-8 r-scalreg@1.0.1 r-hdi@0.1-10 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=SILM
Licenses: GPL 3
Build system: r
Synopsis: Simultaneous Inference for Linear Models
Description:

Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.

r-spacci 1.0.4
Propagated dependencies: r-seurat@5.3.1 r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pheatmap@1.0.13 r-patchwork@1.3.2 r-nnls@1.6 r-matrix@1.7-4 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-fnn@1.1.4.1 r-dplyr@1.1.4 r-circlize@0.4.16
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SpaCCI
Licenses: GPL 2+
Build system: r
Synopsis: Spatially Aware Cell-Cell Interaction Analysis
Description:

This package provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like CellChat <http://www.cellchat.org/>, CellPhoneDB <https://www.cellphonedb.org/>, Cellinker <https://www.rna-society.org/cellinker/index.html>, ICELLNET <https://github.com/soumelis-lab/ICELLNET>, and ConnectomeDB <https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales.

r-ssmodels 2.0.1
Propagated dependencies: r-sn@2.1.1 r-rdpack@2.6.4 r-pracma@2.4.6 r-numderiv@2016.8-1.1 r-misctools@0.6-28
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://fsbmat-ufv.github.io/ssmodels/
Licenses: GPL 2+
Build system: r
Synopsis: Sample Selection Models
Description:

In order to facilitate the adjustment of the sample selection models existing in the literature, we created the ssmodels package. Our package allows the adjustment of the classic Heckman model (Heckman (1976), Heckman (1979) <doi:10.2307/1912352>), and the estimation of the parameters of this model via the maximum likelihood method and two-step method, in addition to the adjustment of the Heckman-t models introduced in the literature by Marchenko and Genton (2012) <doi:10.1080/01621459.2012.656011> and the Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016) <doi:10.1111/sjos.12171>. We also implemented functions to adjust the generalized version of the Heckman model, introduced by Bastos, Barreto-Souza, and Genton (2021) <doi:10.5705/ss.202021.0068>, that allows the inclusion of covariables to the dispersion and correlation parameters, and a function to adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020) <doi:10.1080/02664763.2020.1780570> that uses the Birnbaum-Saunders distribution as a joint distribution of the selection and primary regression variables. This package extends and complements existing R packages such as sampleSelection (Toomet and Henningsen, 2008) and ssmrob (Zhelonkin et al., 2016), providing additional robust and flexible sample selection models.

r-sanple 0.2.0
Propagated dependencies: r-scales@1.4.0 r-salso@0.3.57 r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/laura-dangelo/SANple
Licenses: Expat
Build system: r
Synopsis: Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo
Description:

Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

r-spselect 0.0.1
Propagated dependencies: r-tester@0.3.0 r-pracma@2.4.6 r-magic@1.6-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spselect
Licenses: GPL 2+
Build system: r
Synopsis: Selecting Spatial Scale of Covariates in Regression Models
Description:

Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models. All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.

r-support-bws 0.4-6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=support.BWS
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Case 1 Best-Worst Scaling
Description:

This package provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) <doi:10.1017/CBO9781107337855> for details on best-worst scaling, and Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394> for the package.

r-setartree 0.2.1
Propagated dependencies: r-generics@0.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/rakshitha123/setartree
Licenses: Expat
Build system: r
Synopsis: SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
Description:

The implementation of a forecasting-specific tree-based model that is in particular suitable for global time series forecasting, as proposed in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the concept of Self Exciting Threshold Autoregressive (SETAR) models to define the node splits and thus, the model is named SETAR-Tree. The SETAR-Tree uses some time-series-specific splitting and stopping procedures. It trains global pooled regression models in the leaves allowing the models to learn cross-series information. The depth of the tree is controlled by conducting a statistical linearity test as well as measuring the error reduction percentage at each node split. Thus, the SETAR-Tree requires minimal external hyperparameter tuning and provides competitive results under its default configuration. A forest is developed by extending the SETAR-Tree. The SETAR-Forest combines the forecasts provided by a collection of diverse SETAR-Trees during the forecasting process.

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