Feature List

Full List of Features


Standard-features

  • Easy-to-use Excel interface
  • Missing value imputation with 20-nearest neighbors
  • 2D plots of additive effects for visualizing nonlinearity
  • 3D plots for visualizing interactions
  • Overall fit measures GoF
  • Fit measures structural model: R2
  • Fit measures measurement model: Crombachs Alpha, AEV, Composite Reliability
  • Path strenght measures (standarized and unstandardized): Average Simulated Effect (ASE), OEAD, Linear Path Coefficient, Absolute Maximum Effect
  • Factor Scores
  • Polinomial formula for every nonlinearity
  • Interaction strength effect IE
  • T-values and significance level for ASE, OEAD, LPC, IE
  • Confidence intervales 10% – 90%
  • Nominal variables as gender can be incorporated like every other MV
  • Estimates Total Effects by summarizing direct and indirect impacts

Options

  • Reflexive and Formative constructs usable
  • Invert scale of MV to improve readability of plots
  • Normalizing scale to 0-100% to improve readability of plots
  • Disable Bootstrapping and plottings
  • Over-weighting of rare cases.
  • Cross-validation and Hold-One-Out
  • Specify No. of interations
  • Specify NN size and size of Committee-of-Networks
  • Specify apriori probablility of paths
  • Linear Partial Least Squares

Modules

  • Second-order models
  • Hierarchical Bayes for calculation of individual OEAD (path strength)
  • CAA Competitive Advantage Analysis
  • TS time serial data analysis
  • Missing Imputation
  • Case weightning
  • Segment-wise modeling
  • 2-Stage Least Square correction (… of endogenious variables by using instrumental exogenious variables. This enables true models although important variables that influence several endogenious variables are missing.)

Additional revolutionary features

  • Causal Direction Discovery: Evaluates bidirectional paths and potentially identifies true causal direction.
  • Universal Multi-Target Regression: Regression method that can handle fewer cases than input variables by regressing on multiple target variables simioutaniously.

 

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