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BiProbitPartial (version 1.0.3)

Bivariate Probit with Partial Observability

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) .

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Version

Install

install.packages('BiProbitPartial')

Monthly Downloads

24

Version

1.0.3

License

GPL-3

Maintainer

Michael Guggisberg

Last Published

January 10th, 2019

Functions in BiProbitPartial (1.0.3)

grad1

Gradient of bivariate probit with partial observability
llhood1

log likelihood of bivariate probit with partial observability
BiProbitPartial-package

BiProbitPartial: Bivariate Probit with Partial Observability
BiProbitPartial

Bivariate probit with partial observability
SimDat

This is data to be included in my package
predict.BiProbitPartialb

predict method for class 'BiProbitPartialb'
MCMC1

MCMC algorithm to sample from bivariate probit with partial observability
predict.BiProbitPartialf

predict method for class 'BiProbitPartialf'
summary.optimrml

Summary method for class 'optimrml'