A package to perform segmentation using constrained HMM
The functions are used for change-point problems, after an
initial set of change-points within the data has already been
obtained. The function postCP obtains estimates of posterior
probabilities of change-point and hidden states for each
observation, and confidence intervals for the positions of the
change-point. The function postCPsample obtains random samples
of sets of change-points using the output of the postCP
function.

A package to estimate posterior probabilities in change-point
models using constrained HMM

A package to estimate posterior probabilities and perform model
selection in change-point models using constrained HMM.
The functions are used for change-point problems, after an
initial set of change-points within the data has already been
obtained. The function postCP obtains estimates of posterior
probabilities of change-point and hidden states for each
observation, and confidence intervals for the positions of the
change-point. The function postCPsample obtains random samples
of sets of change-points using the output of the postCP
function. Model selection may be performed using function
postCPmodelsel through criteria obtained from function
postCPcrit.

The functions are used for change-point problems, after an initial set of change-points within the data has already been obtained. The function postCP obtains estimates of posterior probabilities of change-point and hidden states for each observation, and confidence intervals for the positions of the change-point. The function postCPsample obtains random samples of sets of change-points using the output of the postCP function. Model selection may be performed using function postCPmodelsel through criteria obtained from function postCPcrit.

Post Double Selection with Double Machine Learning
Implements post double selection using double machine learning,
see Chernozhukov, Chetverikov, Demirer, Duflo,
Hansen, Newey, Robins (2017) <doi:10.1111/ectj.12097>,
for various models, using several back ends. Allows the user flexibility
in specifying their own methods for estimation.
glmnet, grf, neuralnet, progress, ggplot2, stats, parallel,
doParallel