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r-spmc 0.3.15
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spMC
Licenses: GPL 2+
Synopsis: Continuous-Lag Spatial Markov Chains
Description:

This package provides a set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

r-spev 1.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SPEV
Licenses: GPL 2+
Synopsis: Unsmoothed and Smoothed Penalized PCA using Nesterov Smoothing
Description:

We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns.

r-spnn 1.2.1
Propagated dependencies: r-rcpparmadillo@14.0.2-1 r-rcpp@1.0.13-1 r-mass@7.3-61
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spnn
Licenses: GPL 2+
Synopsis: Scale Invariant Probabilistic Neural Networks
Description:

Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.

r-spfa 1.0
Propagated dependencies: r-rcpparmadillo@14.0.2-1 r-rcpp@1.0.13-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spfa
Licenses: Expat
Synopsis: Semi-Parametric Factor Analysis
Description:

Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) <doi:10.1007/s11336-021-09832-8> and Liu & Wang (2023) <arXiv:2303.10079>. Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support OpenMP'. Both continuous and unordered categorical response variables are supported.

r-spgs 1.0-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spgs
Licenses: GPL 2+
Synopsis: Statistical Patterns in Genomic Sequences
Description:

This package provides a collection of statistical hypothesis tests and other techniques for identifying certain spatial relationships/phenomena in DNA sequences. In particular, it provides tests and graphical methods for determining whether or not DNA sequences comply with Chargaff's second parity rule or exhibit purine-pyrimidine parity. In addition, there are functions for efficiently simulating discrete state space Markov chains and testing arbitrary symbolic sequences of symbols for the presence of first-order Markovianness. Also, it has functions for counting words/k-mers (and cylinder patterns) in arbitrary symbolic sequences. Functions which take a DNA sequence as input can handle sequences stored as SeqFastadna objects from the seqinr package.

r-spsp 0.2.0
Propagated dependencies: r-rcpp@1.0.13-1 r-ncvreg@3.15.0 r-matrix@1.7-1 r-lars@1.3 r-glmnet@4.1-8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://xiaorui.site/SPSP/
Licenses: GPL 2+
Synopsis: Selection by Partitioning the Solution Paths
Description:

An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized estimators.

r-spup 1.4-0
Propagated dependencies: r-whisker@0.4.1 r-raster@3.6-30 r-purrr@1.0.2 r-mvtnorm@1.3-2 r-magrittr@2.0.3 r-gstat@2.1-2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spup
Licenses: GPL 3+
Synopsis: Spatial Uncertainty Propagation Analysis
Description:

Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.

r-spm2 1.1.3
Propagated dependencies: r-spm@1.2.2 r-sp@2.1-4 r-randomforest@4.7-1.2 r-nlme@3.1-166 r-gstat@2.1-2 r-glmnet@4.1-8 r-gbm@2.2.2 r-fields@16.3 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spm2
Licenses: GPL 2+
Synopsis: Spatial Predictive Modeling
Description:

An updated and extended version of spm package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

r-spcp 1.3
Propagated dependencies: r-rcpparmadillo@14.0.2-1 r-rcpp@1.0.13-1 r-mvtnorm@1.3-2 r-msm@1.8.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spCP
Licenses: GPL 2+
Synopsis: Spatially Varying Change Points
Description:

This package implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper on arXiv by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <arXiv:1811.11038>.

r-spas 2025.2.1
Propagated dependencies: r-tmb@1.9.15 r-reshape2@1.4.4 r-rcppeigen@0.3.4.0.2 r-plyr@1.8.9 r-numderiv@2016.8-1.1 r-msm@1.8.2 r-matrix@1.7-1 r-mass@7.3-61 r-checkmate@2.3.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SPAS
Licenses: GPL 2+
Synopsis: Stratified-Petersen Analysis System
Description:

The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) <doi:10.2307/2332748>. When s<t, SPAS implements the methods described in Plante, Rivest, and Tremblay (1988) <doi:10.2307/2533994>. Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program of the same name.

r-spass 1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spass
Licenses: GPL 2+ GPL 3+
Synopsis: Study Planning and Adaptation of Sample Size
Description:

Sample size estimation and blinded sample size reestimation in Adaptive Study Design.

r-spats 1.0-19
Propagated dependencies: r-spam@2.11-0 r-fields@16.3 r-data-table@1.16.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SpATS
Licenses: GPL 2+ GPL 3+
Synopsis: Spatial Analysis of Field Trials with Splines
Description:

Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.

r-spgwr 0.6-37
Propagated dependencies: r-spdata@2.3.3 r-sp@2.1-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/rsbivand/spgwr/
Licenses: GPL 2+
Synopsis: Geographically Weighted Regression
Description:

This package provides functions for computing geographically weighted regressions are provided, based on work by Chris Brunsdon, Martin Charlton and Stewart Fotheringham.

r-spiro 0.2.3
Propagated dependencies: r-xml2@1.3.6 r-signal@1.8-1 r-readxl@1.4.3 r-knitr@1.49 r-ggplot2@3.5.1 r-digest@0.6.37 r-cowplot@1.1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/ropensci/spiro
Licenses: Expat
Synopsis: Manage Data from Cardiopulmonary Exercise Testing
Description:

Import, process, summarize and visualize raw data from metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing.

r-spemd 0.1-1
Propagated dependencies: r-spdep@1.3-6 r-sp@2.1-4 r-mba@0.1-2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/pierreroudier/spemd
Licenses: GPL 3
Synopsis: Bi-Dimensional Implementation of the Empirical Mode Decomposition for Spatial Data
Description:

This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and can be applied on spatial data. It can handle both gridded or un-gridded datasets.

r-spiga 1.0.0
Propagated dependencies: r-ga@3.2.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SPIGA
Licenses: GPL 2
Synopsis: Compute SPI Index using the Methods Genetic Algorithm and Maximum Likelihood
Description:

Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.

r-spfda 0.9.1
Propagated dependencies: r-mathjaxr@1.6-0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dipterix/spfda
Licenses: Expat
Synopsis: Function-on-Scalar Regression with Group-Bridge Penalty
Description:

This package implements a group-bridge penalized function-on-scalar regression model proposed by Wang et al. (2020) <arXiv:2006.10163>, to simultaneously estimate functional coefficient and recover the local sparsity.

r-spurs 2.0.2
Propagated dependencies: r-mass@7.3-61 r-lattice@0.22-6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spuRs
Licenses: GPL 3
Synopsis: Functions and Datasets for "Introduction to Scientific Programming and Simulation Using R"
Description:

This package provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.

r-spray 1.0-27
Propagated dependencies: r-stringr@1.5.1 r-rcpp@1.0.13-1 r-partitions@1.10-7 r-magic@1.6-1 r-disordr@0.9-8-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/RobinHankin/spray
Licenses: GPL 2+
Synopsis: Sparse Arrays and Multivariate Polynomials
Description:

Sparse arrays interpreted as multivariate polynomials. Uses disordR discipline (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>). To cite the package in publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.10848>.

r-spant 3.3.0
Propagated dependencies: r-stringr@1.5.1 r-signal@1.8-1 r-rniftyreg@2.8.4 r-rnifti@1.7.0 r-ptw@1.9-16 r-pracma@2.4.4 r-plyr@1.8.9 r-numderiv@2016.8-1.1 r-nloptr@2.1.1 r-mmand@1.6.3 r-minpack-lm@1.2-4 r-jsonlite@1.8.9 r-irlba@2.3.5.1 r-fields@16.3 r-expm@1.0-0 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://spantdoc.wilsonlab.co.uk/
Licenses: GPL 3
Synopsis: MR Spectroscopy Analysis Tools
Description:

This package provides tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

r-sparr 2.3-16
Propagated dependencies: r-spatstat-utils@3.1-1 r-spatstat-univar@3.1-1 r-spatstat-random@3.3-2 r-spatstat-geom@3.3-3 r-spatstat-explore@3.3-3 r-spatstat@3.2-1 r-misc3d@0.9-1 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://tilmandavies.github.io/sparr/
Licenses: GPL 2+
Synopsis: Spatial and Spatiotemporal Relative Risk
Description:

This package provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.

r-spina 4.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://spina.sf.net/
Licenses: Modified BSD
Synopsis: Structure Parameter Inference Approach
Description:

Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.

r-spcov 1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spcov
Licenses: GPL 2
Synopsis: Sparse Estimation of a Covariance Matrix
Description:

This package provides a covariance estimator for multivariate normal data that is sparse and positive definite. Implements the majorize-minimize algorithm described in Bien, J., and Tibshirani, R. (2011), "Sparse Estimation of a Covariance Matrix," Biometrika. 98(4). 807--820.

r-sprex 1.4.2
Propagated dependencies: r-swfscmisc@1.6.6 r-ggplot2@3.5.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/EricArcher/sprex
Licenses: GPL 2+ GPL 3+
Synopsis: Species Richness and Extrapolation
Description:

This package provides functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices.

Total results: 415