hdcd: High-dimensional change point detection
In mlondschien/hdcd: High - Dimensional Change Point Detection

Find breaks in distribution in a possiblity high-dimensional time series

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hdcd(x, delta = 0.1, gamma = 0, lambda = NULL, method = NULL,
  optimizer = "line_search", segmentation = "BS",
  control = hdcd_control())

`x`	A matrix with observations in rows
`delta`	Minimal relative segment length, defaults to 0.1
`gamma`	Minimal required gain to split in the Binary Segmentation Procedure. Defaults to 0.
`lambda`	Tuning parameter passed to the method.
`method`	Method to be applied for change point detection. One of `glassocd`, `RFcd`, `kNNcd`, `custom_classifier`, `custom_gain_function`, `custom_best_split_function`.
`optimizer`	Optimization method to be used to find an (approximate) of the maximum of the gain function. One of `line_search` for BS or similar, `section_search` for OBS or similar or `two_step_search`.
`segmentation`	Segmentation method to be used. One of `BS`, `SBS` or `WBS`.
`control`	Control parameter as returned by hdcd_control