mcp: Regression with Multiple Change Points

Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) doi:10.31219/osf.io/fzqxv and generalizes the approach described in Carlin, Gelfand, & Smith (1992) doi:10.2307/2347570 and Stephens (1994) doi:10.2307/2986119 .

Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) doi:10.31219/osf.io/fzqxv and generalizes the approach described in Carlin, Gelfand, & Smith (1992) doi:10.2307/2347570 and Stephens (1994) doi:10.2307/2986119 .

See also

Useful links:

• https://lindeloev.github.io/mcp/

• Report bugs at https://github.com/lindeloev/mcp/issues

Author

Maintainer: Jonas Kristoffer Lindeløv jonas@lindeloev.dk (ORCID)

Authors:

• Jonas Kristoffer Lindeløv jonas@lindeloev.dk (ORCID)