Title: Change Point Estimation for Clustered Signals
Author: Hongyuan Cao, Wei Biao Wu, and Shannon T.  Holloway
Description: 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.

Description: 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

Title: Markov Model for the Online Multi-Channel Attribution Problem
Description: 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>.

Description: 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 k-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++.