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r-senstrat
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Sensitivity analysis in unmatched observational studies, with or without strata. The main functions are sen2sample() and senstrat(). See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .
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2023-06-16 |
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r-sempower
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Provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM). Moshagen & Erdfelder (2016) <doi:10.1080/10705511.2014.950896>.
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2023-06-16 |
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r-searchable
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Provides functionality for searching / subsetting and slicing named objects using 'stringr/i'-style modifiers by case (in)sensitivity, regular expressions or fixed expressions; searches uses the standard '[' operator and allows specification of default search behavior to either the search target (named object) and/or the search pattern.
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2023-06-16 |
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r-scrime
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Tools for the analysis of high-dimensional data developed/implemented at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME). Main focus is on SNP data. But most of the functions can also be applied to other types of categorical data.
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2023-06-16 |
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r-sciplot
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A collection of functions that creates graphs with error bars for data collected from one-way or higher factorial designs.
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2023-06-16 |
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r-schumaker
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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.
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2023-06-16 |
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r-scdhlm
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Provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. Functions are provided for calculating standardized mean difference effect sizes that are directly comparable to standardized mean differences estimated from between-subjects randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; and Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>. Includes an interactive web interface.
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2023-06-16 |
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r-saspect
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A statistical method for significant analysis of comparative proteomics based on LC-MS/MS Experiments
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2023-06-16 |
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r-sample.size
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Computes the required sample size using the optimal designs with multiple constraints proposed in Mayo et al.(2010). This optimal method is designed for two-arm, randomized phase II clinical trials, and the required sample size can be optimized either using fixed or flexible randomization allocation ratios.
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2023-06-16 |
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r-sparsegrid
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SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
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2023-06-16 |
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r-softclustering
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It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
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2023-06-16 |
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r-sns
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Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided.
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2023-06-16 |
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r-snn
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Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
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2023-06-16 |
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r-smpracticals
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Contains the datasets and a few functions for use with the practicals outlined in Appendix A of the book Statistical Models (Davison, 2003, Cambridge University Press). The practicals themselves can be found at <http://statwww.epfl.ch/davison/SM/>.
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2023-06-16 |
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r-sldassay
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Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.
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2023-06-16 |
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r-ship
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The SHIP-package allows the estimation of various types of shrinkage covariance matrices. These types differ in terms of the so-called covariance target (to be chosen by the user), the highly structured matrix which the standard unbiased sample covariance matrix is shrunken towards and which optionally incorporates prior biological knowledge extracted from the database KEGG. The shrinkage intensity is obtained via an analytical procedure.
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2023-06-16 |
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r-shinybusy
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Add a global indicator (spinner, progress bar, gif) in your 'shiny' applications to show the user that the server is busy.
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2023-06-16 |
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r-shinybootstrap2
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Provides Bootstrap 2 web components for use with the Shiny package. With versions of Shiny prior to 0.11, these Bootstrap 2 components were included as part of the package. Later versions of Shiny include Bootstrap 3, so the Bootstrap 2 components have been moved into this package for those uses who rely on features specific to Bootstrap 2.
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2023-06-16 |
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r-sensitivitymult
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Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>. Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
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2023-06-16 |
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r-segmented
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Given a regression model, segmented `updates' it by adding one or more segmented (i.e., piece-wise linear) relationships. Several variables with multiple breakpoints are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>).
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2023-06-16 |
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r-seasic
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This package conducts Score Equity Assessment (SEA; Dorans, 2004) by calculating and plotting multiple SEA indices as introduced by a variety of authors and summarized by Huggins and Penfield (2012).
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2023-06-16 |
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r-scriptests
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Support for using .Rt (transcript) tests in the tests directory of a package. Provides more convenience and features than the standard .R/.Rout.save tests. Tests can be run under R CMD check and also interactively. Provides source.pkg() for quickly loading code, DLLs, and data from a package for use in an edit/compile/test development cycle.
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2023-06-16 |
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r-screenclean
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Routines for a collection of screen-and-clean type variable selection procedures, including UPS and GS.
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2023-06-16 |
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r-scraep
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A utility to webscrape the in-house careers of members of the European parliament, from its website <http://www.europarl.europa.eu>.
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2023-06-16 |
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r-scma
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Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
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2023-06-16 |
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r-scalreg
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Algorithms for fitting scaled sparse linear regression and estimating precision matrices.
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2023-06-16 |
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r-sasmarkdown
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Settings and functions to extend the 'knitr' 'SAS' engine.
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2023-06-16 |
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r-sascii
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Using any importation code designed for SAS users to read ASCII files into sas7bdat files, the SAScii package parses through the INPUT block of a (.sas) syntax file to design the parameters needed for a read.fwf function call. This allows the user to specify the location of the ASCII (often a .dat) file and the location of the .sas syntax file, and then load the data frame directly into R in just one step.
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2023-06-16 |
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r-spurs
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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.
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2023-06-16 |
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r-sphericalk
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Spherical K-function for point-pattern analysis on the sphere.
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2023-06-16 |
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r-spatialacc
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Provides a set of spatial accessibility measures from a set of locations (demand) to another set of locations (supply). It aims, among others, to support research on spatial accessibility to health care facilities. Includes the locations and some characteristics of major public hospitals in Greece.
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2023-06-16 |
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r-sparsestep
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Implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) <https://arxiv.org/abs/1701.06967>. In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter.
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2023-06-16 |
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r-sparseindextracking
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Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.
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2023-06-16 |
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r-sparkr
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Provides an R Front end for 'Apache Spark' <https://spark.apache.org>.
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2023-06-16 |
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r-spacyr
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An R wrapper to the 'Python' 'spaCy' 'NLP' library, from <http://spacy.io>.
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2023-06-16 |
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r-sorptionanalysis
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Provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.
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2023-06-16 |
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r-sokoban
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Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations.
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2023-06-16 |
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r-soilhyp
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Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7]. Main references: [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, Mualem (1976) <doi:10.1029/WR012i003p00513>, [3] Peters (2013) <doi:10.1002/wrcr.20548>, Iden and Durner (2013) <doi:10.1002/2014WR015937>, [5] Peters (2014) <doi:10.1002/2014WR015937>, [6] Wind G. P. (1966), [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and [8] Brooks and Corey (1964).
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2023-06-16 |
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r-sodavis
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Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.
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2023-06-16 |
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r-snapshot
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Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
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2023-06-16 |
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r-smartsizer
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A set of tools for determining the necessary sample size in order to identify the optimal dynamic treatment regime in a sequential, multiple assignment, randomized trial (SMART). Utilizes multiple comparisons with the best methodology to adjust for multiple comparisons. Designed for an arbitrary SMART design. Please see Artman (2018) <arXiv:1804.04587> for more details.
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2023-06-16 |
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r-slickr
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Create and customize interactive carousels using the 'Slick' JavaScript library and the 'htmlwidgets' package. The carousels can contain plots produced in R, images, 'iframes', videos and other 'htmlwidgets'. These carousels can be used directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
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2023-06-16 |
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r-sjdbc
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Provides a database-independent JDBC interface.
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2023-06-16 |
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r-sin
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This package provides routines to perform SIN model selection as described in Drton & Perlman (2004, 2008). The selected models are represented in the format of the 'ggm' package, which allows in particular parameter estimation in the selected model.
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2023-06-16 |
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r-simile
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Allows a Simile model saved as a compiled binary to be loaded, parameterized, executed and interrogated. This version works with Simile v5.97 on.
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2023-06-16 |
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r-signifreg
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Provide consistent significance controlled variable selection procedure with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, and p-value). The algorithm selects a final model with only significant variables based on a correction choice of False Discovery Rate, Bonferroni, or no correction.
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2023-06-16 |
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r-shinywidgets
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Collection of custom input controls and user interface components for 'Shiny' applications. Give your applications a unique and colorful style !
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2023-06-16 |
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r-shinycustomloader
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A custom css/html or gif/image file for the loading screen in R 'shiny'. It also can use the marquee to have custom text loading screen.
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2023-06-16 |
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r-sgr
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The package for Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
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2023-06-16 |
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r-seqicp
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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.
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2023-06-16 |
