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) .


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install.packages("BiProbitPartial")

1.0.3 by Michael Guggisberg, 5 months ago


Browse source code at https://github.com/cran/BiProbitPartial


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