Bayes Linear Estimators for Finite Population
c(person("Pedro", "Soares Figueiredo",
role = c("aut", "cre"),
email = "pedrosfig@hotmail.com",
comment = c(ORCID = "0000-0003-2279-2881")),
person("Kelly C.", "M. Gonçalves",
role = c("aut", "ths"),
email = "kelly@dme.ufrj.br",
comment = c(ORCID = "0000-0002-4524-547X")))
Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and
the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement) and to the Ratio estimator (using auxiliary
information) - BLE_Ratio().
The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg()
and it is also possible to achieve the design based estimators using vague prior distributions.
Based on Gonçalves, K.C.M, Moura, F.A.S and  Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.
Pedro Soares Figueiredo [aut, cre]
(<https://orcid.org/0000-0003-2279-2881>),
Kelly C. M. Gonçalves [aut, ths]
(<https://orcid.org/0000-0002-4524-547X>)

Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and
the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary
information) - BLE_Ratio() - and to categorical data - BLE_Categorical().
The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg()
and it is also possible to achieve the design based estimators using vague prior distributions.
Based on Gonçalves, K.C.M, Moura, F.A.S and  Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.

Different Models of Posterior Distributions of Adjusted Odds
Ratio
Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed by Chu et al. (2006) <doi:10.1016/j.annepidem.2006.04.001> and Chu et al. (2010) <doi:10.1177/0272989X09353452>.
dplyr, ggplot2, rstan (>= 2.16.2), lme4,