It is a R package for the detection of the point of a
sharp turn of the behavior of the time series.

person("Michael", "Marks", email = "michaelmarks@analyticaconsulting.com", role = c("aut", "cre"))
A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.
Rcpp (>= 1.0.4), dplyr, purrr, tidyr, magrittr, bench, rlang,
ggplot2

Change Point Estimation for Clustered Signals
Hongyuan Cao, Wei Biao Wu, and Shannon T.  Holloway
A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values.

A multiple testing procedure for clustered alternative hypotheses.
It is assumed that the p-values under the null hypotheses follow U(0,1) and
that the distributions of p-values from the alternative hypotheses are
stochastically smaller than U(0,1). By aggregating information, this
method is more sensitive to detecting signals of low magnitude than
standard methods. Additionally, sporadic small p-values appearing
within a null hypotheses sequence are avoided by averaging on the
neighboring p-values.

Markov Model for the Online Multi-Channel Attribution Problem
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. It's called the online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a first-order Markov representation to identifying structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++. For a more extensive version of the package, which includes a k-order Markov model and useful functions to handle customer journey data, and for customizations please contact <davide.altomare@gmail.com>.