Estimate networks and causal relationships in complex systems through
  Structural Equation Modeling. This package also includes functions for importing,
  weight, manipulate, and fit biological network models within the
  Structural Equation Modeling framework as outlined in the Supplementary Material of
  Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.

An assortment of helper functions for doing structural equation
  modeling, mainly by 'lavaan' for now. Most of them are time-saving functions
  for common tasks in doing structural equation modeling and reading the
  output. This package is not for functions that implement advanced statistical
  procedures. It is a light-weight package for simple functions that do simple
  tasks conveniently, with as few dependencies as possible.

Contains methods to generate and evaluate semi-artificial data sets. 
 Based on a given data set different methods learn data properties using machine learning algorithms and
 generate new data with the same properties.
 The package currently includes the following data generators:
  i) a RBF network based generator using rbfDDA() from package 'RSNNS',
  ii) a Random Forest based generator for both classification and regression problems
  iii) a density forest based generator for unsupervised data
 Data evaluation support tools include:
  a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
  b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM
  c) evaluation based on classification performance with various learning models, e.g., random forests.

Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) <arXiv:2204.10426>.