simstudy0.9.2 package

Simulation of Study Data

addColumns

Add columns to existing data set

addCompRisk

Generating single competing risk survival variable

addCondition

Add a single column to existing data set based on a condition

addCorData

Add correlated data to existing data.table

addCorFlex

Create multivariate (correlated) data - for general distributions

addCorGen

Create multivariate (correlated) data - for general distributions

addDataDensity

Add data from a density defined by a vector of integers

addMarkov

Add Markov chain

addMultiFac

Add multi-factorial data

addPeriods

Create longitudinal/panel data

addSynthetic

Add synthetic data to existing data set

betaGetShapes

Convert beta mean and precision parameters to two shape parameters

blockDecayMat

Create a block correlation matrix

blockExchangeMat

Create a block correlation matrix with exchangeable structure

catProbs

Generate Categorical Formula

defCondition

Add single row to definitions table of conditions that will be used to...

defData

Add single row to definitions table

defDataAdd

Add single row to definitions table that will be used to add data to a...

defMiss

Definitions for missing data

defRead

Read external csv data set definitions

defReadAdd

Read external csv data set definitions for adding columns

defReadCond

Read external csv data set definitions for adding columns

defRepeat

Add multiple (similar) rows to definitions table

defRepeatAdd

Add multiple (similar) rows to definitions table that will be used to ...

defSurv

Add single row to survival definitions

delColumns

Delete columns from existing data set

distributions

Distributions for Data Definitions

gammaGetShapeRate

Convert gamma mean and dispersion parameters to shape and rate paramet...

genCatFormula

Generate Categorical Formula

genCluster

Simulate clustered data

genCorData

Create correlated data

genCorFlex

Create multivariate (correlated) data - for general distributions

genCorGen

Create multivariate (correlated) data - for general distributions

genCorMat

Create a correlation matrix

genCorOrdCat

Generate correlated ordinal categorical data

genData

Calling function to simulate data

genDataDensity

Generate data from a density defined by a vector of integers

genDummy

Create dummy variables from a factor or integer variable

genFactor

Create factor variable from an existing (non-double) variable

genFormula

Generate a linear formula

genMarkov

Generate Markov chain

genMiss

Generate missing data

genMixFormula

Generate Mixture Formula

genMultiFac

Generate multi-factorial data

genNthEvent

Generate event data using longitudinal data, and restrict output to ti...

genObs

Create an observed data set that includes missing data

genOrdCat

Generate ordinal categorical data

genSpline

Generate spline curves

genSurv

Generate survival data

genSynthetic

Generate synthetic data

grouped

Mark parameters as grouped

iccRE

Generate variance for random effects that produce desired intra-class ...

logisticCoefs

Determine intercept, treatment/exposure and covariate coefficients tha...

mergeData

Merge two data.tables without modifying inputs

negbinomGetSizeProb

Convert negative binomial mean and dispersion parameters to size and p...

scenario_list

Create list of parameter scenarios

simstudy-deprecated

Deprecated functions in simstudy

simstudy-package

simstudy: Simulation of Study Data

survGetParams

Get survival curve parameters

survParamPlot

Plot survival curves

trimData

Trim longitudinal data file once an event has occurred

trtAssign

Assign treatment

trtObserve

Observed exposure or treatment

trtStepWedge

Assign treatment for stepped-wedge design

updateDef

Update definition table

updateDefAdd

Update definition table

viewBasis

Plot basis spline functions

viewSplines

Plot spline curves

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).

  • Maintainer: Keith Goldfeld
  • License: GPL-3
  • Last published: 2026-02-09 08:40:02 UTC