| cDPA | R Documentation | 
A constrained dynamic programming algorithm (cDPA) can be used to compute the best segmentation with respect to the Poisson likelihood, subject to a constraint on the number of segments, and the changes which must alternate: up, down, up, down, ...
cDPA(count, weight = rep(1, 
    length(count)), maxSegments)| count | Integer vector of  | 
| weight | Data weights (normally this is the number of base pairs). | 
| maxSegments | Maximum number of segments to consider. | 
Toby Dylan Hocking, Guillem Rigaill
fit <- cDPA(c(0, 10, 11, 1), maxSegments=3)
stopifnot(fit$ends[3,4] == 3)
stopifnot(fit$ends[2,3] == 1)
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