Convert a result to JSON

The whole result as JSON, under a documented and versioned schema, for software that consumes changepoint results rather than a person reading them.

Usage

as_json(x, ...)

# S3 method for class 'ggcpt'
as_json(x, pretty = TRUE, data = TRUE, assumptions = TRUE, digits = NA, ...)

# Default S3 method
as_json(x, ...)

Arguments

x

A ggcpt object.

...

Ignored.

pretty

Indent the output? Defaults to TRUE.

data

Include the series itself (index, value and, where the engine supplied one, fitted)? Defaults to TRUE; without it the output is the answer alone, a few hundred bytes.

assumptions

Include cpt_assumptions()? Defaults to TRUE.

digits

Significant digits for numbers. Defaults to NA, the full precision needed for an exact round trip.

Value

A single string of class "json".

Schema 1.0.0

One object with these fields. Every field is always present; one that does not apply is null, so a reader never has to test for a key.

schema, schema_version

"ggchangepoint.ggcpt" and the version, "1.0.0".

method, engine, engine_version, ggchangepoint_version, r_version, created

what produced the result, and when (ISO 8601).

change_in, family

what was detected, and the distribution family if one was asked for.

penalty

{"type", "value"}.

cp_convention

"left": a changepoint is the last observation of its segment.

n

the series length.

index

{"class", "label"} of the time index, or null. Dates are written as ISO 8601 strings and timestamps with their offset.

changepoints

an array of objects, one per changepoint: cp (position) and cp_value, with cp_index when there is an index, and every engine-specific column (ci_lower, ci_upper, posterior_prob, ...).

segments

seg_id, start, end, n, param_estimate.

regions, coefficients, constraints

the optional slots, as arrays of objects (or an object), or null.

diagnostics

residual_dependence (Ljung-Box p_value, lag, acf1), na_omitted and expected_false_positives where recorded.

assumptions

the cpt_assumptions() rows, or null.

call

the call that made the result, as text.

data

{"index", "value", "fitted"} arrays, or null.

See also

cpt_export() to write a file, cpt_import() to read one back, cpt_report(format = "json").

Other result class: annotate_segments(), as_cpt_series(), as_ggcpt(), cpt_annotations(), cpt_export(), cpt_verify(), ggchangepoint-conditions, is_ggcpt(), new_ggcpt(), print.ggcpt()

Examples

set.seed(1)
fit <- cpt_detect(c(rnorm(50), rnorm(50, 3)), method = "pelt")
cat(as_json(fit, data = FALSE))
#> {
#>   "schema": "ggchangepoint.ggcpt",
#>   "schema_version": "1.0.0",
#>   "method": "pelt",
#>   "engine": "changepoint",
#>   "engine_version": "2.3",
#>   "ggchangepoint_version": "0.6.0",
#>   "r_version": "4.6.1",
#>   "created": "2026-09-26T14:14:32+0000",
#>   "change_in": "mean",
#>   "family": null,
#>   "penalty": {
#>     "type": "MBIC",
#>     "value": null
#>   },
#>   "cp_convention": "left",
#>   "n": 100,
#>   "index": null,
#>   "changepoints": [
#>     {
#>       "cp": 50,
#>       "cp_value": 0.881107726454215
#>     }
#>   ],
#>   "segments": [
#>     {
#>       "seg_id": 1,
#>       "start": 1,
#>       "end": 50,
#>       "n": 50,
#>       "param_estimate": 0.100448279960256
#>     },
#>     {
#>       "seg_id": 2,
#>       "start": 51,
#>       "end": 100,
#>       "n": 50,
#>       "param_estimate": 3.11732645386905
#>     }
#>   ],
#>   "regions": null,
#>   "coefficients": null,
#>   "constraints": null,
#>   "diagnostics": {
#>     "residual_dependence": {
#>       "p_value": 0.80524816626966,
#>       "statistic": 6.1180662037446,
#>       "lag": 10,
#>       "acf1": -0.00382933266738007
#>     }
#>   },
#>   "assumptions": [
#>     {
#>       "component": "residual_dependence",
#>       "value": 0.80524816626966,
#>       "flag": false,
#>       "detail": "Ljung-Box at lag 10; lag-1 autocorrelation 0",
#>       "advice": null
#>     },
#>     {
#>       "component": "scale_sensitivity",
#>       "value": 0.973588001682784,
#>       "flag": false,
#>       "detail": "noise sd 0.974; this engine's cost assumes unit noise",
#>       "advice": null
#>     },
#>     {
#>       "component": "expected_false_positives",
#>       "value": 0,
#>       "flag": false,
#>       "detail": "measured on pure noise at n = 1,000 (50 replicates)",
#>       "advice": null
#>     },
#>     {
#>       "component": "count_plausibility",
#>       "value": 1,
#>       "flag": false,
#>       "detail": "1 changepoints in 100 observations (1 per hundred)",
#>       "advice": null
#>     },
#>     {
#>       "component": "data_type",
#>       "value": null,
#>       "flag": false,
#>       "detail": "looks continuous; family gaussian",
#>       "advice": null
#>     }
#>   ],
#>   "call": "cpt_detect(x = c(rnorm(50), rnorm(50, 3)), method = \"pelt\")",
#>   "data": null
#> }