R: Simulating Longitudinal Data with Causal Inference Applications

# Simulating Longitudinal Data with Causal Inference Applications

## Documentation for package ‘simcausal’ version 0.5.7

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

simcausal-package | Simulating Longitudinal Data with Causal Inference Applications
+.DAG | Adding Node(s) to DAG
A | Subsetting/Indexing Actions Defined for 'DAG' Object
action | Define and Add Actions (Interventions)
add.action | Define and Add Actions (Interventions)
add.nodes | Adding Node(s) to DAG
DAG.empty | Initialize an empty DAG object
Define_sVar | Class for defining and evaluating user-specified summary measures (exprs_list)
DF.to.long | Convert Data from Wide to Long Format Using 'reshape'
DF.to.longDT | Faster Conversion of Data from Wide to Long Format Using 'dcast.data.table'
distr.list | List All Custom Distribution Functions in 'simcausal'.
doLTCF | Missing Variable Imputation with Last Time Point Value Carried Forward (LTCF)
eval.target | Evaluate the True Value of the Causal Target Parameter
igraph.to.sparseAdjMat | Convert igraph Network Object into Sparse Adjacency Matrix
N | Subsetting/Indexing 'DAG' Nodes
net.list | List All Custom Network Generator Functions in 'simcausal'.
NetInd.to.sparseAdjMat | Convert Network IDs Matrix into Sparse Adjacency Matrix
NetIndClass | R6 class for creating and storing a friend matrix (network IDs) for network data
network | Define a Network Generator
node | Create Node Object(s)
parents | Show Node Parents Given DAG Object
plotDAG | Plot DAG
plotSurvEst | (EXPERIMENTAL) Plot Discrete Survival Function(s)
print.DAG | Print DAG Object
print.DAG.action | Print Action Object
print.DAG.node | Print DAG.node Object
rbern | Random Sample from Bernoulli Distribution
rcat.b0 | Random Sample from Base 1 (rcat.b1) or Base 0 (rcat.b0) Categorical (Integer) Distribution
rcat.b1 | Random Sample from Base 1 (rcat.b1) or Base 0 (rcat.b0) Categorical (Integer) Distribution
rcat.factor | Random Sample for a Categorical Factor
rcategor | Random Sample for a Categorical Factor
rcategor.int | Random Sample from Base 1 (rcat.b1) or Base 0 (rcat.b0) Categorical (Integer) Distribution
rconst | Constant (Degenerate) Distribution (Returns its Own Argument 'const')
rdistr.template | Template for Writing Custom Distribution Functions
rnet.gnm | Call 'igraph::sample_gnm' to Generate Random Graph Object According to the G(n,m) Erdos-Renyi Model
rnet.gnp | Call 'igraph::sample_gnp' to Generate Random Graph Object According to the G(n,p) Erdos-Renyi Model
rnet.SmWorld | Call 'igraph::sample_smallworld' to Generate Random Graph Object from the Watts-Strogatz Small-World Model
set.DAG | Create and Lock DAG Object
set.targetE | Define Non-Parametric Causal Parameters
set.targetMSM | Define Causal Parameters with a Working Marginal Structural Model (MSM)
sim | Simulate Observed or Full Data from 'DAG' Object
simcausal | Simulating Longitudinal Data with Causal Inference Applications
simfull | Simulate Full Data (From Action DAG(s))
simobs | Simulate Observed Data
sparseAdjMat.to.igraph | Convert Network from Sparse Adjacency Matrix into igraph Object
sparseAdjMat.to.NetInd | Convert Network from Sparse Adjacency Matrix into Network IDs Matrix
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