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r-sinib
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Density, distribution function, quantile function and random generation for the sum of independent non-identical binomial distribution with parameters \code{size} and \code{prob}.
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2024-01-16 |
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r-sinaplot
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The sinaplot is a data visualization chart suitable for plotting any single variable in a multiclass data set. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class.
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2024-01-16 |
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r-simplermarkdown
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Runs R-code present in a pandoc markdown file and includes the resulting output in the resulting markdown file. This file can then be converted into any of the output formats supported by pandoc. The package can also be used as an engine for writing package vignettes.
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2024-01-16 |
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r-simule
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This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.
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2024-01-16 |
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r-simulator
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A framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at <arXiv:1607.00021>.
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2024-01-16 |
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r-simtimer
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Handles datetimes as integers for the usage inside Discrete-Event Simulations (DES). The conversion is made using the internally generic function as.numeric() of the base package. DES is described in Simulation Modeling and Analysis by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.
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2024-01-16 |
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r-simsurv
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Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper <doi:10.18637/jss.v097.i03>. Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
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2024-01-16 |
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r-simmer.plot
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A set of plotting methods for 'simmer' trajectories and simulations.
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2024-01-16 |
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r-simsem
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Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
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2024-01-16 |
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r-simsalapar
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Tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations.
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2024-01-16 |
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r-simplicialcubature
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Provides methods to integrate functions over m-dimensional simplices in n-dimensional Euclidean space. There are exact methods for polynomials and adaptive methods for integrating an arbitrary function.
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2024-01-16 |
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r-simplesetup
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When working across multiple machines and, similarly for reproducible research, it can be time consuming to ensure that you have all of the needed packages installed and loaded and that the correct working directory is set. 'simpleSetup' provides simple functions for making these tasks more straightforward.
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2024-01-16 |
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r-simdesign
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Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, and implicitly supports parallel processing. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
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2024-01-16 |
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r-simcomp
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Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).
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2024-01-16 |
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r-simplercache
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Simple result caching in R based on R.cache. The global environment is not considered when caching results simplifying moving files between multiple instances of R. Relies on more base functions than R.cache (e.g. cached results are saved using saveRDS() and readRDS()).
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2024-01-16 |
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r-simpleneural
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Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
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2024-01-16 |
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r-simplegraph
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Simple classic graph algorithms for simple graph classes. Graphs may possess vertex and edge attributes. 'simplegraph' has no dependencies and it is written entirely in R, so it is easy to install.
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2024-01-16 |
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r-simplecache
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Provides intuitive functions for caching R objects, encouraging reproducible, restartable, and distributed R analysis. The user selects a location to store caches, and then provides nothing more than a cache name and instructions (R code) for how to produce the R object. Also provides some advanced options like environment assignments, recreating or reloading caches, and cluster compute bindings (using the 'batchtools' package) making it flexible enough for use in large-scale data analysis projects.
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2024-01-16 |
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r-simpleboot
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Simple bootstrap routines.
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2024-01-16 |
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r-simms
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Algorithms to create prognostic biomarkers using biological genesets or networks.
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2024-01-16 |
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r-simml
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A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().
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2024-01-16 |
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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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2024-01-16 |
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r-similaritymeasures
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Functions to run and assist four different similarity measures. The similarity measures included are: longest common subsequence (LCSS), Frechet distance, edit distance and dynamic time warping (DTW). Each of these similarity measures can be calculated from two n-dimensional trajectories, both in matrix form.
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2024-01-16 |
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r-simhaz
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Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
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2024-01-16 |
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r-simexaft
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Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
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2024-01-16 |
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r-sigr
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Succinctly and correctly format statistical summaries of various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.
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2024-01-16 |
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r-simex
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Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994) <doi:10.1080/01621459.1994.10476871> and MCSIMEX by Küchenhoff, Mwalili & Lesaffre (2006) <doi:10.1111/j.1541-0420.2005.00396.x>.
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2024-01-16 |
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r-simdistr
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Assessment of the distributions of baseline continuous and categorical variables in randomised trials. This method is based on the Carlisle-Stouffer method with Monte Carlo simulations. It calculates p-values for each trial baseline variable, as well as combined p-values for each trial - these p-values measure how compatible are distributions of trials baseline variables with random sampling. This package also allows for graphically plotting the cumulative frequencies of computed p-values. Please note that code was partly adapted from Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>.
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2024-01-16 |
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r-sidrar
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Allows the user to connect with IBGE's (Instituto Brasileiro de Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information) SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de Recuperacao Automatica" and is the system where IBGE turns available aggregate data from their researches.
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2024-01-16 |
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r-simcop
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Provides a framework to generating random variates from arbitrary multivariate copulae, while concentrating on (bivariate) extreme value copulae. Particularly useful if the multivariate copulae are not available in closed form.
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2024-01-16 |
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r-showtextdb
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Providing font files that can be used by the 'showtext' package.
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2024-01-16 |
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r-simcausal
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A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.
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2024-01-16 |
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r-simboot
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Provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.
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2024-01-16 |
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r-sigora
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Pathway Analysis is statistically linking observations on the molecular level to biological processes or pathways on the systems(i.e., organism, organ, tissue, cell) level. Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative. However, this can lead to identifying spurious pathways as statistically significant since components are often shared amongst pathways. SIGORA seeks to avoid this pitfall by focusing on genes or gene pairs that are (as a combination) specific to a single pathway. In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. The current version allows for pathway analysis of human and mouse datasets. In addition, it contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories and mechanisms for extracting GPS for user-supplied repositories.
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2024-01-16 |
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r-sigoptr
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Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.
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2024-01-16 |
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r-signifreg
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Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.
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2024-01-16 |
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r-sigmoid
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Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
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2024-01-16 |
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r-sightabilitymodel
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Uses logistic regression to model the probability of detection as a function of covariates. This model is then used with observational survey data to estimate population size, while accounting for uncertain detection. See Steinhorst and Samuel (1989).
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2024-01-16 |
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r-sigclust
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SigClust is a statistical method for testing the significance of clustering results. SigClust can be applied to assess the statistical significance of splitting a data set into two clusters. For more than two clusters, SigClust can be used iteratively.
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2024-01-16 |
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r-sig
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Print function signatures and find overly complicated code.
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2024-01-16 |
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r-sievetest
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Functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.
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2024-01-16 |
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r-sieveph
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Implements semiparametric estimation and testing procedures for a continuous, possibly multivariate, mark-specific hazard ratio (treatment/placebo) of an event of interest in a randomized treatment efficacy trial with a time-to-event endpoint, as described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328 337 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4): 606-25 <doi:10.1007/s10985-015-9353-9>. The former considers continuous multivariate marks fully observed in all subjects who experience the event of interest, whereas the latter extends the previous work to allow multivariate marks that are subject to missingness-at-random. For models with missing marks, two estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases, and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the expected profile score vectors for subjects with missing marks. The augmented IPW estimator is doubly robust and recommended for use with incomplete mark data. The methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results.
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2024-01-16 |
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r-shinyvalidate
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Improves the user experience of Shiny apps by helping to provide feedback when required inputs are missing, or input values are not valid.
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2024-01-16 |
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r-sier
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Methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.
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2024-01-16 |
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r-siebanxicor
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Allows to retrieve time series of all indicators available in the Bank of Mexico's Economic Information System (<http://www.banxico.org.mx/SieInternet/>).
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2024-01-16 |
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r-si
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An implementation of four stochastic methods of integrating in R, including: 1. Stochastic Point Method (or Monte Carlo Method); 2. Mean Value Method; 3. Important Sampling Method; 4. Stratified Sampling Method. It can be used to estimate one-dimension or multi-dimension integration by Monte Carlo methods. And the estimated variance (precision) is given. Reference: Caflisch, R. E. (1998) <doi:10.1017/S0962492900002804>.
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2024-01-16 |
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r-shuffle
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Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
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2024-01-16 |
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r-shotgroups
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Analyzes shooting data with respect to group shape, precision, and accuracy. This includes graphical methods, descriptive statistics, and inference tests using standard, but also non-parametric and robust statistical methods. Implements distributions for radial error in bivariate normal variables. Works with files exported by 'OnTarget PC/TDS', 'Silver Mountain' e-target, 'ShotMarker' e-target, or 'Taran', as well as with custom data files in text format. Supports inference from range statistics such as extreme spread. Includes a set of web-based graphical user interfaces.
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2024-01-16 |
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r-showimage
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Sometimes it is handy to be able to view an image file on an 'R' graphics device. This package just does that. Currently it supports 'PNG' files.
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2024-01-16 |
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r-shipunov
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A collection of functions for data manipulation, plotting and statistical computing, to use separately or with the book "Visual Statistics. Use R!": Shipunov (2020) <http://ashipunov.info/shipunov/software/r/r-en.htm>. Dr Alexey Shipunov died in December 2022. Most useful functions: Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering; Recode() which does multiple recoding in a fast, simple and flexible way; Misclass() which outputs confusion matrix even if classes are not concerted; Overlap() which measures group separation on any projection; Biarrows() which converts any scatterplot into biplot; and Pleiad() which is fast and flexible correlogram.
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2024-01-16 |
