Fetches the benchmark of van den Burg and Williams (2020) — real series
from many domains, each annotated independently by several human
annotators — and caches it under tools::R_user_dir(). The
multi-annotator structure is the point: scoring against a single "true"
set silently discards the disagreement, and
cpt_metrics_annotated() is built to keep it.
Arguments
- name
Dataset name, a character vector of names, or
NULL(the default) for the whole catalogue.- cache_dir
Where to cache. Defaults to
tools::R_user_dir("ggchangepoint", "cache").- refresh
Re-download even when a cached copy exists? Defaults to
FALSE.- quiet
Suppress progress messages. Defaults to
FALSE.
Value
With name = NULL, a tibble catalogue (name,
n_obs, n_dim, n_annotators, cached).
Otherwise a named list of list(series, annotations, index,
longname) datasets, in the shape cpt_benchmark() takes.
Network access and licences
This function downloads from GitHub, so it needs a working connection and is skipped in every example and test here. The data belong to their original owners under their own licences; this package neither bundles nor redistributes them. A few TCPD series are not in the repository at all (their sources do not permit redistribution) and are reported as unavailable rather than silently omitted.
References
van den Burg GJJ, Williams CKI (2020). “An evaluation of change point detection algorithms.” arXiv preprint arXiv:2003.06222. doi:10.48550/arXiv.2003.06222 .
Examples
# Not run by R CMD check: every call downloads from the Turing Change
# Point Dataset's GitHub repository, and an example must not require
# network access.
if (FALSE) { # \dontrun{
cpt_load_tcpd() # the catalogue
d <- cpt_load_tcpd("nile") # one dataset
cpt_benchmark(d, methods = c("pelt", "amoc"))
} # }