Package {camplet}

Type: Package
Title: Seasonal Adjustment via the CAMPLET Method
Version: 1.0.0
Description: Seasonal adjustment with CAMPLET (Abeln and Jacobs (2023) <doi:10.1007/978-3-031-22845-2>), a transparent, adaptive method that corrects for seasonality and outliers via arithmetic-progression updates, without model identification or optimisation, and without revising earlier adjusted values when new observations arrive. A pure R port of the original 'Excel' 'VBA' implementation, validated against its output.
URL: https://gitrepo.service.rug.nl/team-data-science/camplet-r
License: GPL-3
Encoding: UTF-8
Depends: R (≥ 3.5)
Suggests: testthat (≥ 3.0.0), readxl
Config/testthat/edition: 3
Config/roxygen2/version: 8.0.0
NeedsCompilation: no
Packaged: 2026-10-07 13:31:55 UTC; herbert
Author: Barend Abeln [aut] (CAMPLET method and original VBA implementation), Herbert Kruitbosch [aut, cre]
Maintainer: Herbert Kruitbosch <datascience@rug.nl>
Repository: CRAN
Date/Publication: 2026-10-22 16:30:08 UTC

Coerce a 'camplet' result to a data frame

Description

Coerce a 'camplet' result to a data frame

Usage

## S3 method for class 'camplet'
as.data.frame(x, ..., diagnostics = TRUE)

Arguments

x

a 'camplet' object, as returned by [camplet()].

...

unused.

diagnostics

if 'TRUE' (default), include the per-period adaptive diagnostic columns (see [camplet()]) alongside 'raw'/'sa'/'seasonal'.

Value

a 'data.frame' with one row per input observation: the columns of 'x$data', plus the diagnostic columns if 'diagnostics = TRUE'.

CAMPLET seasonal adjustment

Description

Seasonally adjusts a single time series with the CAMPLET algorithm: a transparent, adaptive method that corrects for seasonality and outliers via arithmetic-progression updates, without model identification or optimisation. See Abeln and Jacobs (2023) for the method.

Usage

camplet(
  x,
  dates = NULL,
  periodicity = NULL,
  ca = 0,
  mu = 50,
  pa = 1,
  le = 0,
  ti = 1
)

Arguments

x

a numeric vector or 'ts' object, without missing values.

dates

optional 'Date' or 'POSIXct' vector, same length as 'x'; required only when 'periodicity = 31' (calendar-aware, variable-month-length mode for daily data). If supplied, it is checked for regular spacing (see Input checks) and used as the 'time' column of the result.

periodicity

periods per year (e.g. '12', '4'), a cycle length in rows (e.g. '7' for day-of-week seasonality), or '31' for calendar-aware daily mode. If 'x' is a 'ts' object this defaults to 'frequency(x)'; otherwise it is required.

ca, mu, pa, le, ti

CAMPLET tuning parameters; see Details. 'ca = 0' and ‘le = 0' mean "derive a default from 'periodicity'" (the algorithm’s own convention).

Details

The five tuning parameters are:

ca

Common Adjustment: weight applied to normal (non-outlier) observations. '0' = derive from periodicity.

mu

Multiplier: scales the correction applied to outliers.

pa

Pattern-shift length in years.

le

Limit-to-Error: the percent threshold separating an outlier from a normal observation. '0' = derive from periodicity.

ti

Times: number of successive same-direction outliers tolerated before a pattern shift is declared.

Value

an object of class '"camplet"', a list with:

data

a 'data.frame' with one row per input observation: 'time' (if available), 'raw', 'sa' (seasonally adjusted) and 'seasonal'.

diagnostics

a 'data.frame', one row per input observation, with the per-period adaptive-parameter trace the original VBA tool wrote to its "Adj_" worksheets: 'n', 'x', 'ca', 'mu', 'pa', 'le', 'ti', 'expo_e', 'abs_pct_e', 'years', 'weight', 'growth'.

accuracy

CAMPLET's own accuracy metric (0-100).

average_extrapolation_error, outlier_percentage

diagnostic summary scalars feeding into 'accuracy'.

parameter_summary

character, the final effective '"ca / mu / pa / le / ti"'.

Input checks

CAMPLET reads observations by position: the k-th value is taken to be the period after the (k-1)-th. 'camplet()' therefore stops with an error when

It warns when an explicit 'periodicity' contradicts monthly or quarterly data, i.e. 'frequency(x)' or the spacing of 'dates', such as 'periodicity = 4' for a monthly series. A plain numeric 'x' without 'dates' cannot be checked: making sure it has no gaps is up to the caller.

References

Abeln, B. and Jacobs, J. P. A. M. (2023). *Seasonal Adjustment Without Revisions: A Real-Time Approach*. SpringerBriefs in Economics. Springer, Cham. doi:10.1007/978-3-031-22845-2

Examples

x <- AirPassengers
res <- camplet(x)
res$accuracy
head(as.data.frame(res))

Run the CAMPLET kernel on a single numeric series

Description

Pure R port of the CAMPLET seasonal adjustment algorithm (originally a VBA macro by Abeln, D200603-CampletExcel-v4.2, basCBFherzien; also ported to Java in the jdplus-camplet plugin for JDemetra+ v3). This is the internal numeric engine; end users should call [camplet()].

Usage

camplet_kernel(
  values,
  periodicity,
  dates = NULL,
  ca = 0,
  mu = 50,
  pa = 1,
  le = 0,
  ti = 1
)

Arguments

values

numeric vector, length >= 3 * periodicity.

periodicity

integer. Periods per year (12, 4, ...), a cycle length in rows (e.g. 7 for day-of-week seasonality), or the literal sentinel 31 to switch on calendar-date-aware, variable-month-length warm-up bounds.

dates

optional 'Date' vector, same length as 'values'; required only when 'periodicity == 31'.

ca

Common Adjustment weight for normal observations. '0' = derive from periodicity (1.5 for periodicity 4 or 12, else 1.75).

mu

Multiplier for outlier correction. Default '50'.

pa

Pattern-shift length in years. Default '1'.

le

Limit-to-Error outlier threshold in percent. '0' = derive from periodicity ('5 + periodicity / 4').

ti

Number of successive outliers before a pattern shift is declared. Default '1'.

Value

a list with elements 'sa', 'seasonal', 'growth' (numeric vectors, same length as 'values'), 'accuracy', 'average_extrapolation_error', 'outlier_percentage', 'parameter_summary' and 'diagnostics' (a 'data.frame', one row per input value; the per-period trace that the original VBA tool wrote to its "Adj_" worksheets).