PostProcess: PostProcess
In lorenzha/hdcd: High-Dimensional Changepoint Detection

Adjust the position of each changepoint by refitting the model in the respective segments.

1	PostProcess(x, cpts, delta, lambda, method)

`x`	A n times p matrix or data frame.
`cpts`	A numeric vector containing the found changepoints. Can be of length zero if no changepoints have been found.
`delta`	Numeric value between 0 and 0.5. This tuning parameter determines the minimal segment size proportional to the size of the dataset and hence an upper bound for the number of changepoints (roughly 1/δ).
`lambda`	Positive numeric value. This is the regularization parameter in the single Lasso fits. This value is ignored if FUN is not NULL.
`method`	Which estimator should be used? Possible choices are nodewise_regression: Nodewise regression is based on a single node that needs to be specified with an additional parameter `node` pointing to the column index of the node of interest. Uses `glmnet` internally. See Kovács (2016) for details. summed_regression: Summed nodewise regression sums up the residual variances of nodewise regression over all nodes. Uses `glasso` internally. See Kovács (2016) for details. ratio_regression: Likelihood ratio based regression sums the pseudo-profile-likelihood over all nodes. Uses `glasso` internally. See Kovács (2016) for details. glasso: The graphical Lasso uses the approach of Friedman et al (2007). In contrast to the other approaches the exact likelihood the whole graphical model is computed and used as loss. This value is ignored if `FUN` is not `NULL`.