John Mount has a new package available in R:

In the above we have an input (or independent variable)

`x`

and an observed outcome (or dependent variable)`y_observed`

(portrayed as points).`y_observed`

is the unobserved idea value`y_ideal`

(portrayed by the dashed curve) plus independent noise. The modeling goal is to get close the`y_ideal`

curve using the`y_observed`

observations. Obviously this can be done with a smoothing spline, but let’s use`RcppDynProg`

to find a piecewise linear fit.

To encode this as a dynamic programming problem we need to build a cost matrix that for every consecutive interval of`x`

-values we have estimated the out-of sample quality of fit. This is supplied by the function`RcppDynProg::lin_costs()`

(using the PRESS statistic), but lets take a quick look at the idea.

It’s an interesting package whose purpose is to turn an input data stream into a set of linear functions which approximate the stream. I’m not sure I’ll ever have a chance to use it, but it’s good to know that it’s there if I do ever need it.