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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-sbf
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Smooth Backfitting for additive models using Nadaraya-Watson estimator
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-sasmarkdown
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Settings and functions to extend the 'knitr' 'SAS' engine.
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2025-04-22 |
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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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2025-04-22 |
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r-sas7bdat
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Read SAS files in the sas7bdat data format.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-samplesizeproportions
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A set of R functions for calculating sample size requirements 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.
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2025-04-22 |
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r-samplesizemeans
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A set of R functions for calculating sample size requirements 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.
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2025-04-22 |
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r-samplesizelogisticcasecontrol
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To determine sample size for case-control studies to be analyzed using logistic regression.
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2025-04-22 |
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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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2025-04-22 |
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r-samplesize4clinicaltrials
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The design of phase 3 clinical trials can be classified into 4 types: (1) Testing for equality;(2) Superiority trial;(3) Non-inferiority trial; and (4) Equivalence trial according to the goals. Given that none of the available packages combines these designs in a single package, this package has made it possible for researchers to calculate sample size when comparing means or proportions in phase 3 clinical trials with different designs. The ssc function can calculate the sample size with pre-specified type 1 error rate,statistical power and effect size according to the hypothesis testing framework. Furthermore, effect size is comprised of true treatment difference and non-inferiority or equivalence margins which can be set in ssc function. (Reference: Yin, G. (2012). Clinical Trial Design: Bayesian and Frequentist Adaptive Methods. John Wiley & Sons.)
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
