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r-scoreggum
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Estimate GGUM Person Parameters Using Pre-Calibrated Item Parameters and Binary or Graded Disagree-Agree Responses
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2025-03-25 |
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r-scma
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public |
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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2025-03-25 |
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r-sciviews
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Functions to install SciViews additions to R, and more tools.
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2025-03-25 |
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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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2025-03-25 |
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r-scina
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An automatic cell type detection and assignment algorithm for single cell RNA-Seq and Cytof/FACS data. 'SCINA' is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of markers, such as genes and protein symbols that are highly or lowly expressed in each category. See Zhang Z, et al (2019) <doi:10.3390/genes10070531> for more details.
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2025-03-25 |
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r-scifigure
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Users may specify what fundamental qualities of a new study have or have not changed in an attempt to reproduce or replicate an original study. A comparison of the differences is visualized. Visualization approach follows 'Patil', 'Peng', and 'Leek' (2016) <doi:10.1101/066803>.
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2025-03-25 |
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r-scientotext
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It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.
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2025-03-25 |
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r-scico
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Colour choice in information visualisation is important in order to avoid being mislead by inherent bias in the used colour palette. The 'scico' package provides access to the perceptually uniform and colour-blindness friendly palettes developed by Fabio Crameri and released under the "Scientific Colour-Maps" moniker. The package contains 24 different palettes and includes both diverging and sequential types.
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2025-03-25 |
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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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2025-03-25 |
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r-schorsch
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Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg. For details on the implemented methods, see Roland Pfister and Markus Janczyk (2016) <doi: 10.20982/tqmp.12.2.p147>.
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2025-03-25 |
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r-schoolmath
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Contains functions and datasets for math taught in school. A main focus is set to prime-calculation.
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2025-03-25 |
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r-schoenberg
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Functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.
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2025-03-25 |
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r-scenario
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Uses the neural gas algorithm to construct a scenario tree for use in multi-stage stochastic programming. The primary input is a set of initial scenarios or realizations of a disturbance. The scenario tree nodal structure must be predefined using a scenario tree nodal partition matrix.
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2025-03-25 |
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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>; Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>; and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>. Includes an interactive web interface.
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2025-03-25 |
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r-scdensity
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Implements methods for obtaining kernel density estimates subject to a variety of shape constraints (unimodality, bimodality, symmetry, tail monotonicity, bounds, and constraints on the number of inflection points). Enforcing constraints can eliminate unwanted waves or kinks in the estimate, which improves its subjective appearance and can also improve statistical performance. The main function scdensity() is very similar to the density() function in 'stats', allowing shape-restricted estimates to be obtained with little effort. The methods implemented in this package are described in Wolters and Braun (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) <http://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>. See the scdensity() help for for full citations.
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2025-03-25 |
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r-scbmeanfd
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Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting, bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
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2025-03-25 |
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r-scatterplot3d
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Plots a three dimensional (3D) point cloud.
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2025-03-25 |
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r-scatterd3
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Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive features : panning, zooming, tooltips, etc.
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2025-03-25 |
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r-scam
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Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package 'mgcv' are used for setting up the model matrix, printing and plotting the results. Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.
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2025-03-25 |
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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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2025-03-25 |
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r-scagnostics
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Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot. They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.
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2025-03-25 |
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r-sbsdiff
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Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.
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2025-03-25 |
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r-sbrect
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The package uses fitting axes-aligned rectangles to a time series in order to find structural breaks. The algorithm enclose the time series in a number of axes-aligned rectangles and tries to minimize their area and number. As these are conflicting aims, the user has to specify a parameter alpha in [0.0,1.0]. Values close to 0 result in more breakpoints, values close to 1 in fewer. The left edges of the rectangles are the breakpoints. The package supplies two methods, computeBreakPoints(series,alpha) which returns the indices of the break points and computeRectangles(series,alpha) which returns the rectangles. The algorithm is randomised; it uses a genetic algorithm. Therefore, the break point sequence found can be different in different executions of the method on the same data, especially when used on longer series of some thousand observations. The algorithm uses a range-tree as background data structure which makes i very fast and suited to analyse series with millions of observations. A detailed description can be found in Paul Fischer, Astrid Hilbert, Fast detection of structural breaks, Proceedings of Compstat 2014.
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2025-03-25 |
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r-sbl
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Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.
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2025-03-25 |
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r-sbgcop
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Estimation and inference for parameters in a Gaussian copula model, treating the univariate marginal distributions as nuisance parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. This package also provides a semiparametric imputation procedure for missing multivariate data.
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2025-03-25 |
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r-sbf
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Smooth Backfitting for additive models using Nadaraya-Watson estimator
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2025-03-25 |
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r-saves
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The purpose of this package is to be able to save and load only the needed variables/columns of a dataframe in special binary files (tar archives) - which seems to be a lot faster method than loading the whole binary object (RData files) via load() function, or than loading columns from SQLite/MySQL databases via SQL commands (see vignettes). Performance gain on SSD drives is a lot more sensible compared to basic load() function. The performance improvement gained by loading only the chosen variables in binary format can be useful in some special cases (e.g. where merging data tables is not an option and very different datasets are needed for reporting), but be sure if using this package that you really need this, as non-standard file formats are used!
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2025-03-25 |
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r-saver
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An implementation of a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018) <doi:10.1038/s41592-018-0033-z> for more details.
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2025-03-25 |
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r-sautomata
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Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.
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2025-03-25 |
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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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2025-03-25 |
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r-sasmixed
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Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute.
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2025-03-25 |
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r-sasmarkdown
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Settings and functions to extend the 'knitr' 'SAS' engine.
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2025-03-25 |
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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, this 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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2025-03-25 |
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r-sas7bdat
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Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages (notably the haven package) contain more robust readers for sas7bdat files.
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2025-03-25 |
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r-sarp.moodle
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Provides a set of basic functions for creating Moodle XML output files suited for importing questions in Moodle (a learning management system, see <https://moodle.org/> for more information).
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2025-03-25 |
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r-sanon
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There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
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2025-03-25 |
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r-samplingdatacrt
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Package provides the possibility to sampling complete datasets from a normal distribution to simulate cluster randomized trails for different study designs.
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2025-03-25 |
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r-samplesizeproportions
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Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate the difference between two binomial proportions. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of binomial observations are provided. In all cases, estimation of the difference between two binomial proportions is considered. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L., du Berger R. and Bélisle P. (1997) <doi:10.1002/(sici)1097-0258(19970415)16:7%3C769::aid-sim495%3E3.0.co;2-v>.
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2025-03-25 |
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r-samplesizemeans
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Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate a normal mean or the difference between two normal means. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of normal means are provided. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L. and Bélisle P. (1997) <https://www.jstor.org/stable/2988525>.
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2025-03-25 |
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r-samplesizelogisticcasecontrol
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To determine sample size or power for case-control studies to be analyzed using logistic regression.
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2025-03-25 |
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r-samplesizecmh
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Calculates the power and sample size for Cochran-Mantel-Haenszel tests. There are also several helper functions for working with probability, odds, relative risk, and odds ratio values.
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2025-03-25 |
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r-samplesize4clinicaltrials
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There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals.
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2025-03-25 |
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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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2025-03-25 |
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r-samplesize
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Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
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2025-03-25 |
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r-salty
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Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc.
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2025-03-25 |
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r-saltsampler
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The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC) sampling of random variables on a simplex. A Self-Adjusting Logit Transform (SALT) proposal is used so that sampling is still efficient even in difficult cases, such as those in high dimensions or with parameters that differ by orders of magnitude. Special care is also taken to maintain accuracy even when some coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are included in the package, enabling easy assessment of the convergence and mixing of the chain within the constrained space.
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2025-03-25 |
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r-saic
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Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
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2025-03-25 |
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r-safd
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The aim of the package is to provide some basic functions for doing statistics with one dimensional Fuzzy Data (in the form of polygonal fuzzy numbers). In particular, the package contains functions for the basic operations on the class of fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) as well as for calculating (Bertoluzza) distance and sample variance. Moreover a function to simulate fuzzy random variables and bootstrap tests for the equality of means is included. Version 2.1 fixes some bugs of previous versions.
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2025-03-25 |
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r-saery
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A complete set of functions to calculate several EBLUP (Empirical Best Linear Unbiased Predictor) estimators and their mean squared errors. All estimators are based on an area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). The REML method is used for fitting this model.
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2025-03-25 |
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r-saemix
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The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.
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2025-03-25 |
