two_step_search: Two step search optimisation algorithm
In mlondschien/hdcd: High - Dimensional Change Point Detection
Description
Usage
Arguments
Value
Description
Find an (approximate) maximum of gain_function by repeatedly fitting and evaluating.
Usage
1
two_step_search(gain_function, split_candidates, n, control)
Arguments
gain_function
closure
split_candidates
An array of indices where gain function is (possibly) evaluated.
n
total number of observations
control
Control parameter as returned by hdcd_control
Value
A list with entries gain, max_gain, best_split, permutation_test and pval
mlondschien/hdcd documentation built on Jan. 5, 2021, 11:26 p.m.
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Description Usage Arguments Value
Description
Find an (approximate) maximum of gain_function by repeatedly fitting and evaluating.
Usage
1 | two_step_search(gain_function, split_candidates, n, control)
|
Arguments
gain_function |
closure |
split_candidates |
An array of indices where gain function is (possibly) evaluated. |
n |
total number of observations |
control |
Control parameter as returned by hdcd_control |
Value
A list with entries gain, max_gain, best_split, permutation_test and pval
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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