Flexible Data Simulation Using the Multivariate Normal Distribution
Block diagonal matrix
Block matrix
Block structure
Concordance statistic
Matrix contrast
Expected community structure
Expected concordance statistic
Heatmap visualisation
Simulation of undirected graph
Layered Directed Acyclic Graph
Making positive definite matrix
Matching arguments
Maximising matrix contrast
Within-group probabilities for communities
Receiver Operating Characteristic (ROC) curve
True and False Positive Rates
Receiver Operating Characteristic (ROC)
Simulation of binary contribution status
Simulation of undirected graph with block structure
Simulation of data with underlying clusters
Data simulation for sparse Principal Component Analysis
Simulation of a correlation matrix
Data simulation for Gaussian Graphical Modelling
Simulation of precision matrix
Data simulation for multivariate regression
Data simulation for Structural Causal Modelling
Simulation of symmetric matrix with block structure
Tuning function (logistic regression)
Tuning function (correlation)
Tuning function (covariance)
This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) <doi:10.1093/jrsssc/qlad058>).