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 |
| Reference manual: | BiProbitPartial.pdf |
| Package source: | BiProbitPartial_1.0.3.tar.gz |
| Windows binaries: | r-devel: BiProbitPartial_1.0.3.zip, r-release: BiProbitPartial_1.0.3.zip, r-oldrel: BiProbitPartial_1.0.3.zip |
| macOS binaries: | r-release (arm64): BiProbitPartial_1.0.3.tgz, r-oldrel (arm64): BiProbitPartial_1.0.3.tgz, r-release (x86_64): BiProbitPartial_1.0.3.tgz, r-oldrel (x86_64): BiProbitPartial_1.0.3.tgz |
| Old sources: | BiProbitPartial archive |
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