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>.

methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>=
2.18.1), rstantools (>= 2.1.1), dplyr, ggplot2, lme4

Computation of Bayes factors for interrupted time-series designs
The BayesSingleSub package is a suite of functions for
computing various Bayes factors for interrupted time-series,
based on the models described in de Vries and Morey
(submitted).

The BayesSingleSub package is a suite of functions for computing various Bayes factors for interrupted time-series, based on the models described in de Vries and Morey (2013).

An implementation of methods for spectral analysis using the Bayesian framework. It includes functions for modelling spectrum as well as appropriate plotting and output estimates. There is segmentation capability with RJ MCMC (Reversible Jump Markov Chain Monte Carlo). The package takes these methods predominantly from the 2012 paper "AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series" <DOI:10.1080/01621459.2012.716340>.

MCMC Sampling of Bayesian Linear Models via Summary Statistics
Evgeny Savel'ev, Alexey Miroshnikov, Erin Conlon
Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions:  one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package 'ff' to handle data sets that are too large to fit into a user's physical memory, by reading in data in chunks.