Automatically calculate direct, indirect, and total effects for 
    piecewise structural equation models, comprising lists of fitted models 
    representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). 
    Confidence intervals are provided via bootstrapping.

Sensitivity analysis in structural equation modeling using
    influence measures and diagnostic plots. Support leave-one-out casewise
    sensitivity analysis presented by Pek and MacCallum (2011)
    <doi:10.1080/00273171.2011.561068> and approximate casewise influence
    using scores and casewise likelihood.

A framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.

Estimate networks and causal relationships in complex systems through
  Structural Equation Modeling. This package also includes functions to import,
  weight, manipulate, and fit biological network models within the
  Structural Equation Modeling framework proposed in
  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.