Package: simcausal 0.5.5

Oleg Sofrygin

simcausal: 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.

Authors:Oleg Sofrygin [aut, cre], Mark J. van der Laan [aut], Romain Neugebauer [aut]

simcausal_0.5.5.tar.gz
simcausal_0.5.5.zip(r-4.7)simcausal_0.5.5.zip(r-4.6)simcausal_0.5.5.zip(r-4.5)
simcausal_0.5.5.tgz(r-4.6-any)simcausal_0.5.5.tgz(r-4.5-any)
simcausal_0.5.5.tar.gz(r-4.7-any)simcausal_0.5.5.tar.gz(r-4.6-any)
simcausal_0.5.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
simcausal/json (API)

# Install 'simcausal' in R:

install.packages('simcausal', repos = c('https://osofr.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/osofr/simcausal/issues

On CRAN:

counterfactual-data sem simulated-network simulating-data structural-equations

6.76 score 69 stars 166 scripts 297 downloads 44 exports 16 dependencies

Last updated from:bce5ad71eb. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	182
source / vignettes	OK	240
linux-release-x86_64	NOTE	199
macos-release-arm64	NOTE	154
macos-oldrel-arm64	NOTE	160
windows-devel	NOTE	165
windows-release	NOTE	176
windows-oldrel	NOTE	156
wasm-release	OK	136

Exports:A action add.action add.nodes DAG.empty DF.to.long DF.to.longDT distr.list doLTCF eval.target igraph.to.sparseAdjMat N net.list NetInd.to.sparseAdjMat NetIndClass network node parents plotDAG plotSurvEst rbern rcat.b0 rcat.b1 rcat.factor rcategor rcategor.int rconst rdistr.template rnet.gnm rnet.gnp rnet.SmWorld set.DAG set.targetE set.targetMSM sim simfull simobs sparseAdjMat.to.igraph sparseAdjMat.to.NetInd vecfun.add vecfun.all.print vecfun.print vecfun.remove vecfun.reset

Dependencies:assertthat cli cpp11 data.table glue igraph lattice lifecycle magrittr Matrix pkgconfig R6 rlang stringi stringr vctrs

simcausal Package: Simulations with Complex Longitudinal Data (Technical Details and Extended Examples)

Introduction | Introduction | Technical details | Technical details | Simulation study with single time point interventions | Simulation study with single time point interventions | Simulation study with multiple time point interventions | Simulation study with multiple time point interventions | Replication study of the comparative performances of two estimators | Replication study of the comparative performances of two estimators | Replication study of the impact of misspecification of propensity score models | Replication study of the impact of misspecification of propensity score models | Discussion | Discussion

Rendered fromsimcausal_vignette.Rnwusingknitr::knitr

Last update: 2022-10-15
Started: 2015-06-11

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

Package: simcausal 0.5.5

simcausal: Simulating Longitudinal Data with Causal Inference Applications

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)