Bivariate Probit with Partial Observability

A suite of functions to estimate, summarize and perform predictions with the bivariate probit subject to partial observability. The frequentist and Bayesian probabilistic philosophies are both supported. The frequentist method is estimated with maximum likelihood and the Bayesian method is estimated with a Markov Chain Monte Carlo (MCMC) algorithm developed by Rajbanhdari, A (2014) .


Reference manual

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1.0.3 by Michael Guggisberg, 2 years ago

Browse source code at

Authors: Michael Guggisberg and Amrit Romana

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, Formula, optimr, pbivnorm, mvtnorm, RcppTN, coda

Depends on numDeriv

Suggests sampleSelection

Linking to Rcpp, RcppArmadillo, RcppTN

See at CRAN