Simulating Longitudinal Data with Causal Inference Applications

Documentation for package ‘simcausal’ version 0.1

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simcausal-package	Simulate longitudinal data and evaluate causal parameters
A	Subsetting/indexing actions defined for DAG object
action	Define Actions
add.action	Define Actions
add.nodes	Adding Nodes to DAG
DAG.empty	Initialize an empty DAG object
DF.to.long	Convert data from wide format into long format using 'reshape' base function
DF.to.longDT	Convert 'data.frame' into long format (faster than 'DF.to.long')
distr.list	List all custom distribution functions in 'SimCausal'.
doLTCF	Last Timepoint Carried Forward (LTCF)
eval.target	Evaluate the Causal Target Parameter via Monte-Carlo Simulation
N	Subsetting/indexing DAG nodes
node	Create Node Object(s)
node.depr	Create Node Object(s) (Deprecated)
parents	Show Node Parents Given DAG Object
plotDAG	Plot DAG
plotSurvEst	(EXPERIMENTAL) Plot Survival
print.DAG	Print DAG Object
print.DAG.action	Print Action Object
print.DAG.node	Print DAG.node Object
rbern	Bernoulli node distribution
rcategor	Categorical node distribution (factor)
rcategor.int	Categorical node distribution (integer)
rconst	Constant (degenerate) node distribution
rdistr.template	Template for writing custom distributions
set.DAG	Create and lock DAG object
set.targetE	Node Expectations (E) as the Causal Target Parameter
set.targetMSM	MSMs as the Causal Target Parameter
sim	Simulate from DAG or list of DAGs (Either Action or Observed DAG(s))
simcausal	Simulate longitudinal data and evaluate causal parameters
simfull	Simulate Full Data (From Action/Intervention DAG(s))
simobs	Simulate Observed Data
vecfun.add	Add custom vectorized functions
vecfun.all.print	Print names of all vectorized functions
vecfun.print	Print names of custom vectorized functions
vecfun.remove	Remove custom vectorized functions
vecfun.reset	Reset custom vectorized function list