Title: Detecting structural breaks using rectangle covering
Authors@R: c(person("Paul", "Fischer", role = c("aut", "cre","cph"),
email = "pafi@dtu.dk"),person("Astrid", "Hilbert", role = c("ctb","cph"),
Description: The package uses fitting axes-aligned rectangles to a time series in order to find structural breaks. The algorithm enclose the time series in a number of axes-aligned rectangles and tries to minimize their area and number. As these are conflicting aims, the user has to specify a parameter alpha in [0.0,1.0]. Values close to 0 result in more breakpoints, values close to 1 in fewer. The left edges of the rectangles are the breakpoints. The package supplies two  methods, computeBreakPoints(series,alpha) which returns the indices of the break points and computeRectangles(series,alpha) which returns the rectangles. The algorithm is randomised; it uses a genetic algorithm. Therefore, the break point sequence found can be different in different executions of the method on the same data, especially when used on  longer series of some thousand observations. The algorithm uses a range-tree as background data structure which makes i very fast and suited to analyse series with millions of observations. A detailed description can be found in Paul Fischer, Astrid Hilbert, Fast detection  of structural breaks, Proceedings of Compstat 2014.

Title: Simplified Bayesian Sensitivity Analysis
Author: Davor Cubranic and Paul Gustafson
Description: Simplified Bayesian Sensitivity Analysis
Depends: Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.6)

LinkingTo: Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.6)

Title: Satorra-Bentler Scaled Chi-Squared Difference Test
Author: Frank D. Mann <frankdmann@gmail.com>
Maintainer: Frank D. Mann <frankdmann@gmail.com>
Description: Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.
Packaged: 2018-05-02 20:53:22 UTC; frankdmann