fpopTree: Functional Pruning Optimal Partitioning for data structures...
In vrunge/fpopTree: Multiple change-point detection in tree structures

Functional pruning optimal partitioning with data in a tree structure

1	fpopTree(vertex_data, tree, type = "mean", weights = NULL, testMode = FALSE)

`vertex_data`	vector of data associated to each vertex
`tree`	tree structure encoded in a list
`type`	a string defining the cost model to use: "mean", "variance", "poisson", "exp", "negbin"
`weights`	vector of weights (positive numbers), same size as data
`testMode`	boolean. False by default. Used to debug the code

a gfpop object = (changepoints, states, forced, parameters, globalCost)

changepoints: is the vector of changepoints (we give the last element of each segment)
states: is the vector giving the state of each segment
forced: is the vector specifying whether the constraints of the graph are active (=1) or not (=0)
parameters: is the vector of successive parameters of each segment
globalCost: is a number equal to the total loss: the minimal cost for the optimization problem with all penalty values excluded