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>.

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 in <doi:10.1016/j.annepidem.2006.04.001> and <doi:10.1177/0272989X09353452>.

Provides tools for fitting Bayesian single index models with
    flexible choices of priors for both the index and the link function.
    The package implements model estimation and posterior inference using
    efficient MCMC algorithms built on the 'nimble' framework, allowing
    users to specify, extend, and simulate models in a unified and
    reproducible manner. The following methods are implemented in the
    package: Antoniadis et al. (2004)
    <https://www.jstor.org/stable/24307224>, Wang (2009)
    <doi:10.1016/j.csda.2008.12.010>, Choi et al. (2011)
    <doi:10.1080/10485251003768019>, Dhara et al. (2019)
    <doi:10.1214/19-BA1170>, McGee et al. (2023) <doi:10.1111/biom.13569>.

Implements methods for Bayesian analysis of State Space Models. 
  Includes implementations of the Particle Marginal Metropolis-Hastings 
  algorithm described in Andrieu et al. (2010) 
  <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by 
  Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and
  T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.