Package: simstudy 0.8.1.9000

simstudy: Simulation of Study Data

Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

Authors:Keith Goldfeld [aut, cre], Jacob Wujciak-Jens [aut]

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simstudy.pdf |simstudy.html✨
simstudy/json (API)

# Install 'simstudy' in R:

install.packages('simstudy', repos = c('https://kgoldfeld.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kgoldfeld/simstudy/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

data-generation data-simulation simulation statistical-models

10.98 score 80 stars 1 packages 948 scripts 1.3k downloads 1 mentions 63 exports 13 dependencies

Last updated 4 months agofrom:df880328cb. Checks:OK: 5 NOTE: 3 ERROR: 1. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 27 2024
R-4.5-win-x86_64	ERROR	Oct 27 2024
R-4.5-linux-x86_64	OK	Oct 27 2024
R-4.4-win-x86_64	OK	Oct 27 2024
R-4.4-mac-x86_64	OK	Oct 27 2024
R-4.4-mac-aarch64	OK	Oct 27 2024
R-4.3-win-x86_64	NOTE	Oct 27 2024
R-4.3-mac-x86_64	NOTE	Oct 27 2024
R-4.3-mac-aarch64	NOTE	Oct 27 2024

Exports:addColumns addCompRisk addCondition addCorData addCorFlex addCorGen addDataDensity addMarkov addMultiFac addPeriods addSynthetic betaGetShapes blockDecayMat blockExchangeMat catProbs defCondition defData defDataAdd defMiss defRead defReadAdd defReadCond defRepeat defRepeatAdd defSurv delColumns gammaGetShapeRate genCatFormula genCluster genCorData genCorFlex genCorGen genCorMat genCorOrdCat genData genDataDensity genDummy genFactor genFormula genMarkov genMiss genMixFormula genMultiFac genNthEvent genObs genOrdCat genSpline genSurv genSynthetic iccRE logisticCoefs mergeData negbinomGetSizeProb survGetParams survParamPlot trimData trtAssign trtObserve trtStepWedge updateDef updateDefAdd viewBasis viewSplines

Dependencies:backports BH bigmemory bigmemory.sri data.table fastglm glue mvnfast pbv Rcpp RcppArmadillo RcppEigen uuid

Clustered Data

Rendered fromclustered.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-09-25

Correlation Matrices

Rendered fromcorelationmat.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2023-02-16

Customized Distributions

Rendered fromcustomdist.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2024-05-13
Started: 2024-05-13

Dynamic Data Definition

Rendered fromdouble_dot_extension.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-10-05

Longitudinal Data

Rendered fromlongitudinal.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-09-25

Missing Data

Rendered frommissing.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-09-25

Ordinal Categorical Data

Rendered fromordinal.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-09-25

Simulating Study Data

Rendered fromsimstudy.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2024-06-29
Started: 2016-02-05

Spline Data

Rendered fromspline.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-09-25

Survival Data

Rendered fromsurvival.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-09-25

Targeted logistic model coefficients

Rendered fromlogisticCoefs.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2024-07-29
Started: 2023-06-16

Treatment and Exposure

Rendered fromtreat_and_exposure.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-11-23
Started: 2020-09-25

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add columns to existing data set	addColumns
Generating single competing risk survival variable	addCompRisk
Add a single column to existing data set based on a condition	addCondition
Add correlated data to existing data.table	addCorData
Create multivariate (correlated) data - for general distributions	addCorFlex
Create multivariate (correlated) data - for general distributions	addCorGen
Add data from a density defined by a vector of integers	addDataDensity
Add Markov chain	addMarkov
Add multi-factorial data	addMultiFac
Create longitudinal/panel data	addPeriods
Add synthetic data to existing data set	addSynthetic
Convert beta mean and precision parameters to two shape parameters	betaGetShapes
Create a block correlation matrix	blockDecayMat
Create a block correlation matrix with exchangeable structure	blockExchangeMat
Add single row to definitions table of conditions that will be used to add data to an existing definitions table	defCondition
Add single row to definitions table	defData
Add single row to definitions table that will be used to add data to an existing data.table	defDataAdd
Definitions for missing data	defMiss
Read external csv data set definitions	defRead
Read external csv data set definitions for adding columns	defReadAdd
Read external csv data set definitions for adding columns	defReadCond
Add multiple (similar) rows to definitions table	defRepeat
Add multiple (similar) rows to definitions table that will be used to add data to an existing data.table	defRepeatAdd
Add single row to survival definitions	defSurv
Delete columns from existing data set	delColumns
Distributions for Data Definitions	beta binary binomial categorical distributions exponential gamma mixture negBinomial nonrandom normal noZeroPoisson poisson uniform
Convert gamma mean and dispersion parameters to shape and rate parameters	gammaGetShapeRate
Generate Categorical Formula	genCatFormula
Simulate clustered data	genCluster
Create correlated data	genCorData
Create multivariate (correlated) data - for general distributions	genCorFlex
Create multivariate (correlated) data - for general distributions	genCorGen
Create a correlation matrix	genCorMat
Calling function to simulate data	genData
Generate data from a density defined by a vector of integers	genDataDensity
Create dummy variables from a factor or integer variable	genDummy
Create factor variable from an existing (non-double) variable	genFactor
Generate a linear formula	genFormula
Generate Markov chain	genMarkov
Generate missing data	genMiss
Generate Mixture Formula	genMixFormula
Generate multi-factorial data	genMultiFac
Generate event data using longitudinal data, and restrict output to time until the nth event.	genNthEvent
Create an observed data set that includes missing data	genObs
Generate ordinal categorical data	genOrdCat
Generate spline curves	genSpline
Generate survival data	genSurv
Generate synthetic data	genSynthetic
Generate variance for random effects that produce desired intra-class coefficients (ICCs) for clustered data.	iccRE
Determine intercept, treatment/exposure and covariate coefficients that can be used for binary data generation with a logit link and a set of covariates	logisticCoefs
Merge two data tables	mergeData
Convert negative binomial mean and dispersion parameters to size and prob parameters	negbinomGetSizeProb
Deprecated functions in simstudy	simstudy-deprecated
Get survival curve parameters	survGetParams
Plot survival curves	survParamPlot
Trim longitudinal data file once an event has occurred	trimData
Assign treatment	trtAssign
Observed exposure or treatment	trtObserve
Assign treatment for stepped-wedge design	trtStepWedge
Update definition table	updateDef
Update definition table	updateDefAdd
Plot basis spline functions	viewBasis
Plot spline curves	viewSplines

Package: simstudy 0.8.1.9000

simstudy: Simulation of Study Data

Clustered Data

Correlated Data

Correlation Matrices

Customized Distributions

Dynamic Data Definition

Longitudinal Data

Missing Data

Ordinal Categorical Data

Simulating Study Data

Spline Data

Survival Data

Targeted logistic model coefficients

Treatment and Exposure

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)