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>.

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Description file content

Package
BiProbitPartial
Type
Package
Title
Bivariate Probit with Partial Observability
Version
1.0.3
Date
2019-01-10
Author
Michael Guggisberg and Amrit Romana
Maintainer
Michael Guggisberg
Description
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) .
GPL-3
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)
Depends
numDeriv(>= 2016.8-1)
Suggests
sampleSelection
RoxygenNote
6.1.0
Encoding
UTF-8
NeedsCompilation
yes
Packaged
2019-01-10 13:55:32 UTC; mguggisb
Repository
CRAN
Date/Publication
2019-01-10 22:12:04 UTC

`install.packages('BiProbitPartial')`

1.0.3

5 months ago

Michael Guggisberg

GPL-3

Depends on

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)

sampleSelection