simcausal v0.2.0


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by Oleg Sofrygin

Simulating Longitudinal Data with Causal Inference Applications

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.

Functions in simcausal

Name Description
distr.list List All Custom Distribution Functions in simcausal. Convert Data from Wide to Long Format Using
A Subsetting/Indexing Actions Defined for DAG Object
add.action Define and Add Actions (Interventions)
node Create Node Object(s)
sim Simulate Observed or Full Data from DAG Object
rdistr.template Template for Writing Custom Distribution Functions
DAG.empty Initialize an empty DAG object
vecfun.remove Remove Custom Vectorized Functions
vecfun.all.print Print Names of All Vectorized Functions
set.DAG Create and Lock DAG Object Evaluate the True Value of the Causal Target Parameter
rcategor Categorical Node Distribution (Factor)
add.nodes Adding Node(s) to DAG
plotDAG Plot DAG
set.targetMSM Define Causal Parameters with a Working Marginal Structural Model (MSM)
simfull Simulate Full Data (From Action DAG(s))
set.targetE Define Non-Parametric Causal Parameters
print.DAG.node Print DAG.node Object
rbern Bernoulli Node Distribution
simcausal Simulating Longitudinal Data with Causal Inference Applications
print.DAG.action Print Action Object
vecfun.add Add Custom Vectorized Functions
print.DAG Print DAG Object
vecfun.reset Reset Custom Vectorized Function List
N Subsetting/Indexing DAG Nodes
simobs Simulate Observed Data
parents Show Node Parents Given DAG Object Convert Data from Wide to Long Format Using reshape Categorical Node Distribution (Integer)
vecfun.print Print Names of Custom Vectorized Functions
doLTCF Missing Variable Imputation with Last Time Point Value Carried Forward (LTCF)
plotSurvEst (EXPERIMENTAL) Plot Discrete Survival Function(s)
rconst Constant (Degenerate) Node Distribution
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Last month downloads


Type Package
Date 2015-06-11
VignetteBuilder knitr
License GPL-2
Packaged 2015-06-12 20:13:45 UTC; olegsofrygin
NeedsCompilation no
Repository CRAN
Date/Publication 2015-06-13 00:41:46

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