OPSR0.1.2 package

Ordinal Probit Switching Regression

anova.opsr

ANOVA for OPSR Model Fits

extract.opsr

Extract Method for OPSR Model Fits

loglik_cpp

Interface to C++ Log-Likelihood Implementation

loglik_R

R-based Log-Likelihood Implementation

model.frame.opsr

Extracting the Model Frame from OPSR Model Fits

model.matrix.opsr

Construct Design Matrices for OPSR Model Fits

opsr_2step

Heckman Two-Step Estimation

opsr_check_omp

Check Whether OpenMP is Available

opsr_check_start

Check the User-Specified Starting Values

opsr_max_threads

Check Maximum Number of Threads Available

opsr_null_model

Null Model for OPSR Model fits

opsr_prepare_coefs

Prepares Coefficients for Likelihood Function

opsr_simulate

Simulate Data from an OPSR Process

OPSR-package

OPSR: Ordinal Probit Switching Regression

opsr.fit

Fitter Function for Ordinal Probit Switching Regression Models

opsr

Fitting Ordinal Probit Switching Regression Models

predict.opsr

Predict Method for OPSR Model Fits

print.anova.opsr

Print Method for ANOVA OPSR Objects

print.summary.opsr

Print Method for Summary OPSR Objects

summary.opsr

Summarizing OPSR Model Fits

Download source packageRead PDF manual

Estimates ordinal 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>.

  • Maintainer: Daniel Heimgartner
  • License: GPL (>= 3)
  • Last published: 2024-11-01

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