Ordered Probit Switching Regression
ANOVA for OPSR Model Fits
Extract Method for OPSR Model Fits
Interface to C++ Log-Likelihood Implementation
R-based Log-Likelihood Implementation
Extracting the Model Frame from OPSR Model Fits
Construct Design Matrices for OPSR Model Fits
Heckman Two-Step Estimation
Check Whether OpenMP is Available
Check the User-Specified Starting Values
Check Maximum Number of Threads Available
Null Model for OPSR Model fits
Prepares Coefficients for Likelihood Function
Simulate Data from an OPSR Process
Step Function for OPSR Model Fits
Treatment Effect Computations for OPSR Model Fits
OPSR: Ordered Probit Switching Regression
Fitter Function for Ordered Probit Switching Regression Models
Fitting Ordered Probit Switching Regression Models
Pairs Plot for OPSR TE Objects
Plot Method for OPSR Model Fits
Predict Method for OPSR Model Fits
Print Method for ANOVA OPSR Objects
Print Method for ATE Objects
Print Method for OPSR ATE Objects
Print Method for Summary OPSR Objects
Print Method for Summary OPSR TE Objects
Print Method for TE Objects
Summarizing OPSR Model Fits
Summarizing OPSR TE Objects
Estimates ordered probit switching regression models - a Heckman type selection model with an ordinal selection and continuous outcomes. Different model specifications are allowed for each treatment/regime. For more details on the method, see Wang & Mokhtarian (2024) <doi:10.1016/j.tra.2024.104072> or Chiburis & Lokshin (2007) <doi:10.1177/1536867X0700700202>.