Inference on the marginal model of the mixed effect model with 
    the Box-Cox transformation and on the model median differences between
    treatment groups for longitudinal randomized clinical trials. These 
    statistical methods are proposed by Maruo et al. (2017) 
    <doi:10.1002/sim.7279>.

Provides an implementation of the product partition model described in Barry and Hartigan (2019) <doi:10.2307/2290726> for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) <doi:10.48550/arXiv.1509.00817>, allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in 'bcp' v.3.0.0, is currently not implemented.

The Behavioral Change Point Analysis (BCPA) is a method of
    identifying hidden shifts in the underlying parameters of a time series,
    developed specifically to be applied to animal movement data which is
    irregularly sampled.  The method is based on: E. Gurarie, R. Andrews and 
    K. Laidre A novel method for identifying behavioural changes in animal 
    movement data (2009) Ecology Letters 12:5 395-408. A development version is 
    on <https://github.com/EliGurarie/bcpa>. NOTE: the BCPA method may be useful 
    for any univariate, irregularly sampled Gaussian time-series, but animal 
    movement analysts are encouraged to apply correlated velocity change point 
    analysis as implemented in the smoove package, as of this writing on GitHub 
    at <https://github.com/EliGurarie/smoove>. An example of a univariate analysis
    is provided in the UnivariateBCPA vignette.

Provides functions to utilize a command line utility that does bulk inserts and exports from SQL Server databases.

Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.
             Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. 
             Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.