Introspect available changepoint detection methods

Returns a tibble describing every method the package knows about — those that are wired, those a user has registered with cpt_register_method(), and those that are planned — along with their capabilities and installation status. Useful for discovering what can be run, what needs to be installed, and which methods expose the extras the diagnostics need (confidence intervals, a fitted signal, a posterior, a detector statistic, a solution path, a bandwidth to sweep).

Usage

cpt_methods(capabilities = TRUE)

Arguments

capabilities

Include the capability flag columns? Defaults to TRUE. Set FALSE for the compact 0.4.0-shaped table.

Value

A tibble with columns:

method

Method name as passed to cpt_detect().

change_in

What types of change the method can detect.

engine

The upstream R package that implements the method.

status

"available" (wired in this release), "registered" (supplied by the user this session — see cpt_register_method()), or "planned" (future).

installed

TRUE if the engine package is installed, FALSE if it is a Suggests engine that is missing, NA for planned and registered methods.

target_release

What a planned method is waiting on: a release, or "when on CRAN" when the engine package itself is not available from CRAN. NA for methods that are already wired. Asking cpt_detect() for a planned method reports this rather than claiming the name does not exist.

multivariate, univariate, online, ci, fitted, posterior, statistic, path, scale_space

Capability flags (omitted when capabilities = FALSE). ci means the engine supplies changepoint-location confidence intervals; fitted a length-\(n\) fitted signal; posterior a per-location posterior probability; statistic, path and scale_space the internals rendered by ggcpt_statistic(), ggcpt_solution_path() and ggcpt_scale_space().

See also

cpt_install_engines() to install a whole family of the engines this table reports on; cpt_detect() to run one; cpt_register_method() to add your own.

Examples

cpt_methods()
#> # A tibble: 55 × 15
#>    method   change_in        engine status installed target_release multivariate
#>    <chr>    <chr>            <chr>  <chr>  <lgl>     <chr>          <lgl>       
#>  1 pelt     mean, var, mean… chang… avail… TRUE      NA             FALSE       
#>  2 binseg   mean, var, mean… chang… avail… TRUE      NA             FALSE       
#>  3 segneigh mean, var, mean… chang… avail… TRUE      NA             FALSE       
#>  4 amoc     mean, var, mean… chang… avail… TRUE      NA             FALSE       
#>  5 np       distribution     chang… avail… TRUE      NA             FALSE       
#>  6 ecp      distribution (m… ecp    avail… TRUE      NA             TRUE        
#>  7 fpop     mean             fpop   avail… TRUE      NA             FALSE       
#>  8 wbs      mean             wbs    avail… TRUE      NA             FALSE       
#>  9 wbs2     mean             break… avail… TRUE      NA             FALSE       
#> 10 not      mean, var, slope not    avail… TRUE      NA             FALSE       
#> # ℹ 45 more rows
#> # ℹ 8 more variables: univariate <lgl>, online <lgl>, ci <lgl>, fitted <lgl>,
#> #   posterior <lgl>, statistic <lgl>, path <lgl>, scale_space <lgl>
# which methods can draw a confidence interval?
subset(cpt_methods(), ci)$method
#> [1] "smuce"       "hsmuce"      "strucchange" "segmented"   "nsp"        
#> [6] "mcp"         "bfast"       "taylor"