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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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2025-04-22 |
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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. Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) <http://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
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2025-04-22 |
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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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2025-04-22 |
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r-simscrpiecewise
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Contains two functions for simulating survival data from piecewise exponential hazards with a proportional hazards adjustment for covariates. The first function SimUNIVPiecewise simulates univariate survival data based on a piecewise exponential hazard, covariate matrix and true regression vector. The second function SimSCRPiecewise semi-competing risks data based on three piecewise exponential hazards, three true regression vectors and three matrices of patient covariates (which can be different or the same). This simulates from the Semi-Markov model of Lee et al (2015) given patient covariates, regression parameters, patient frailties and baseline hazard functions.
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2025-04-22 |
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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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2025-04-22 |
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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. Dirichlet probabilities are calculated in certain cases.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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 so dependencies and it is written entirely in R, so it is easy to install.
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2025-04-22 |
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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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2025-04-22 |
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r-simpleboot
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Simple bootstrap routines.
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2025-04-22 |
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r-simphe
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Provides functions to simulate single or multiple, independent or correlated phenotype(s) with additive, dominance effects and their interactions. Also includes functions to generate phenotype(s) with specific heritability. Flexible and user-friendly options for simulation.
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2025-04-22 |
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r-simms
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Algorithms to create prognostic biomarkers using biological genesets or networks.
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2025-04-22 |
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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 Petkova, Tarpey, Su, and Ogden (2017) <doi: 10.1093/biostatistics/kxw035> and "A constrained single-index model for estimating interactions between a treatment and covariates" (under review, 2019) for detail. The main function of this package is simml().
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-sii
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This package calculates ANSI S3.5-1997 Speech Intelligibility Index (SII), a standard method for computing the intelligibility of speech from acoustical measurements of speech, noise, and hearing thresholds. This package includes data frames corresponding to Tables 1 - 4 in the ANSI standard as well as a function utilizing these tables and user-provided hearing threshold and noise level measurements to compute the SII score. The methods implemented here extend the standard computations to allow calculation of SII when the measured frequencies do not match those required by the standard by applying interpolation to obtain values for the required frequencies -- Development of this package was funded by the Center for Bioscience Education and Technology (CBET) of the Rochester Institute of Technology (RIT).
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2025-04-22 |
