BiProbitPartial: 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) <doi:10.1002/9781118771051.ch13>.

Version: 1.0.3
Depends: numDeriv (≥ 2016.8-1)
Imports: Rcpp (≥ 0.12.19), Formula (≥ 1.2-3), optimr (≥ 2016-8.16), pbivnorm (≥ 0.6.0), mvtnorm (≥ 1.0-8), RcppTN (≥ 0.2-2), coda (≥ 0.19-2)
LinkingTo: Rcpp, RcppArmadillo, RcppTN
Suggests: sampleSelection
Published: 2019-01-10
Author: Michael Guggisberg and Amrit Romana
Maintainer: Michael Guggisberg <mguggisb at ida.org>
License: GPL-3
NeedsCompilation: yes
CRAN checks: BiProbitPartial results

Downloads:

Reference manual: BiProbitPartial.pdf
Package source: BiProbitPartial_1.0.3.tar.gz
Windows binaries: r-devel: BiProbitPartial_1.0.3.zip, r-devel-gcc8: BiProbitPartial_1.0.3.zip, r-release: BiProbitPartial_1.0.3.zip, r-oldrel: BiProbitPartial_1.0.3.zip
OS X binaries: r-release: BiProbitPartial_1.0.3.tgz, r-oldrel: BiProbitPartial_1.0.3.tgz
Old sources: BiProbitPartial archive

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