simcausal v0.5.0
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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 | |
| DF.to.long | Convert Data from Wide to Long Format Using reshape | |
| Define_sVar | Class for defining and evaluating user-specified summary measures (exprs_list) | |
| NetInd.to.sparseAdjMat | Convert Network IDs Matrix into Sparse Adjacency Matrix | |
| network | Define a Network Generator | |
| DAG_Class | Class for storing a DAG object (the data generating distribution specified by the SEM) | |
| vecfun.all.print | Print Names of All Vectorized Functions | |
| eval.target | Evaluate the True Value of the Causal Target Parameter | |
| rcategor.int | Categorical Node Distribution (Integer) | |
| parents | Show Node Parents Given DAG Object | |
| sim | Simulate Observed or Full Data from DAG Object | |
| doLTCF | Missing Variable Imputation with Last Time Point Value Carried Forward (LTCF) | |
| set.targetMSM | Define Causal Parameters with a Working Marginal Structural Model (MSM) | |
| set.DAG | Create and Lock DAG Object | |
| add.nodes | Adding Node(s) to DAG | |
| plotDAG | Plot DAG | |
| set.targetE | Define Non-Parametric Causal Parameters | |
| rcategor | Categorical Node Distribution (Factor) | |
| rdistr.template | Template for Writing Custom Distribution Functions | |
| DF.to.longDT | Faster Conversion of Data from Wide to Long Format Using dcast.data.table | |
| igraph.to.sparseAdjMat | Convert igraph Network Object into Sparse Adjacency Matrix | |
| N | Subsetting/Indexing DAG Nodes | |
| sparseAdjMat.to.igraph | Convert Network from Sparse Adjacency Matrix into igraph Object | |
| add.action | Define and Add Actions (Interventions) | |
| simfull | Simulate Full Data (From Action DAG(s)) | |
| print.DAG.node | Print DAG.node Object | |
| A | Subsetting/Indexing Actions Defined for DAG Object | |
| NetIndClass | R6 class for creating and storing a friend matrix (network IDs) for network data | |
| Node_Class | Class for storing a node object (the data generating distribution specified by the SEM) | |
| plotSurvEst | (EXPERIMENTAL) Plot Discrete Survival Function(s) | |
| vecfun.remove | Remove Custom Vectorized Functions | |
| vecfun.print | Print Names of Custom Vectorized Functions | |
| simcausal | Simulating Longitudinal Data with Causal Inference Applications | |
| rconst | Constant (Degenerate) Node Distribution | |
| print.DAG | Print DAG Object | |
| node | Create Node Object(s) | |
| sparseAdjMat.to.NetInd | Convert Network from Sparse Adjacency Matrix into Network IDs Matrix | |
| vecfun.reset | Reset Custom Vectorized Function List | |
| vecfun.add | Add Custom Vectorized Functions | |
| simobs | Simulate Observed Data | |
| rbern | Bernoulli Node Distribution | |
| distr.list | List All Custom Distribution Functions in simcausal. | |
| DAG.empty | Initialize an empty DAG object | |
| print.DAG.action | Print Action Object | |
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Details
| Type | Package |
| URL | https://github.com/osofr/simcausal |
| BugReports | https://github.com/osofr/simcausal/issues |
| VignetteBuilder | knitr |
| License | GPL-2 |
| NeedsCompilation | no |
| Packaged | 2016-02-20 03:23:21 UTC; olegsofrygin |
| Repository | CRAN |
| Date/Publication | 2016-02-20 08:55:22 |
| imports | assertthat , data , data.table , igraph , Matrix , methods , R6 , stringr |
| depends | base (>= 3.2.0) , R (>= 3.2.0) |
| suggests | bindata , copula , ggplot2 , Hmisc , knitr , ltmle , mvtnorm , RUnit , tmlenet |
| Contributors | Oleg Sofrygin, Mark J. van der Laan, Romain Neugebauer |
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