A                       Subsetting/Indexing Actions Defined for 'DAG'
                        Object
DAG.empty               Initialize an empty DAG object
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'
Define_sVar             Class for defining and evaluating
                        user-specified summary measures (exprs_list)
N                       Subsetting/Indexing 'DAG' Nodes
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
add.action              Define and Add Actions (Interventions)
add.nodes               Adding Node(s) to DAG
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
net.list                List All Custom Network Generator Functions in
                        'simcausal'.
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.factor             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.SmWorld            Call 'igraph::sample_smallworld' to Generate
                        Random Graph Object from the Watts-Strogatz
                        Small-World Model
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
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.NetInd
                        Convert Network from Sparse Adjacency Matrix
                        into Network IDs Matrix
sparseAdjMat.to.igraph
                        Convert Network from Sparse Adjacency Matrix
                        into igraph Object
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
