Type:	Package
Title:	Seasonal Adjustment with X-13 in 'JDemetra+ 3.x'
Version:	3.6.0
Description:	R Interface to 'JDemetra+ 3.x' (https://github.com/jdemetra) time series analysis software. It offers full access to options and outputs of 'X-13', including Reg-ARIMA modelling (automatic AutoRegressive Integrated Moving Average (ARIMA) model with outlier detection and trading days adjustment) and X-11 decomposition.
License:	EUPL version 1.1 | EUPL version 1.2 [expanded from: EUPL]
URL:	https://github.com/rjdverse/rjd3x13, https://rjdverse.github.io/rjd3x13/
BugReports:	https://github.com/rjdverse/rjd3x13/issues
Depends:	R (≥ 4.1.0)
Imports:	rJava (≥ 1.0-6), RProtoBuf (≥ 0.4.20), rjd3toolkit (≥ 3.6.0), rjd3jars
Encoding:	UTF-8
RoxygenNote:	7.3.3
SystemRequirements:	Java (>= 17)
Collate:	'deprecated.R' 'print.R' 'regarima_generic.R' 'utils.R' 'regarima_outliers.R' 'regarima_spec.R' 'x13_rslts.R' 'x13_spec.R' 'revisions.R' 'set_x11_spec.R' 'udvar.R' 'x13.R' 'zzz.R'
NeedsCompilation:	no
Packaged:	2026-01-23 09:56:04 UTC; onyxia
Author:	Jean Palate [aut], Alain Quartier-la-Tente [aut], Tanguy Barthelemy [aut, cre, art], Anna Smyk [aut]
Maintainer:	Tanguy Barthelemy <tanguy.barthelemy@insee.fr>
Repository:	CRAN
Date/Publication:	2026-01-27 21:00:13 UTC

Deprecated functions

Description

Deprecated functions

Usage

spec_x13(name = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"))

spec_regarima(name = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"))

spec_x11()

fast_x13(
  ts,
  spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
  context = NULL,
  userdefined = NULL
)

fast_regarima(
  ts,
  spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"),
  context = NULL,
  userdefined = NULL
)

.jx13(
  ts,
  spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"),
  context = NULL,
  userdefined = NULL
)

userdefined_variables_x13(x = c("X-13", "RegArima", "X-11"))

Arguments

Value

All these functions are deprecated and return the same value as the function that replaces them:

• spec_x13() returns the same value as x13_spec()

• spec_regarima() returns the same value as regarima_spec()

• spec_x11() returns the same value as x11_spec()

• fast_x13() returns the same value as x13_fast()

• fast_regarima() returns the same value as regarima_fast()

• .jx13() returns the same value as jx13()

• userdefined_variables_x13() returns the same value as x13_dictionary()

Java version.

Description

Java version.

Usage

current_java_version

minimal_java_version

Format

An object of class integer of length 1.

An object of class numeric of length 1.

Value

current_java_version is the current Java version and minimal_java_version is the minimum accepted Java version.

Examples

print(minimal_java_version)
print(current_java_version)

Java Utility Functions

Description

These functions are used in all JDemetra+ 3.0 packages to easily interact between R and Java objects.

Usage

.x13_rslts(jrslts)

.jd2r_spec_x11(jspec)

.r2jd_spec_x11(spec)

.r2jd_spec_regarima(spec)

.jd2r_spec_regarima(jspec)

.r2jd_spec_x13(spec)

.jd2r_spec_x13(jspec)

Arguments

Value

These functions return specification in Java, proto or R.

Refresh a specification with constraints

Description

Functions x13_refresh and regarima_refresh allow to create a new specification by updating a specification used for a previous estimation. Some selected parameters will be kept fixed (previous estimation results) while others will be freed for re-estimation in a domain of constraints. See details and examples.

Usage

regarima_refresh(
  spec,
  refspec = NULL,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"),
  period = 0,
  start = NULL,
  end = NULL
)

x13_refresh(
  spec,
  refspec = NULL,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"),
  period = 0,
  start = NULL,
  end = NULL
)

Arguments

Details

The selection of constraints to be kept fixed or re-estimated is called a revision policy. User-defined parameters are always copied to the new refreshed specifications. In X-13 only the reg-arima part can be refreshed. X-11 decomposition will be completely re-run, keeping all the user-defined parameters from the original specification.

Available refresh policies are:

1. Current: applying the current pre-adjustment reg-arima model and handling the new raw data points, or any sub-span of the series as Additive Outliers (defined as new intervention variables);

2. Fixed: applying the current pre-adjustment reg-arima model and replacing forecasts by new raw data points;

3. FixedParameters: pre-adjustment reg-arima model is partially modified: regression coefficients will be re-estimated but regression variables, Arima orders and coefficients are unchanged;

4. FixedAutoRegressiveParameters: same as FixedParameters but Arima Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR) coefficients are kept fixed;

5. FreeParameters: all regression and Arima model coefficients are re-estimated, regression variables and Arima orders are kept fixed;

6. Outliers: regression variables and Arima orders are kept fixed, but outliers will be re-detected on the defined span, thus all regression and Arima model coefficients are re-estimated;

7. Outliers_StochasticComponent: same as "Outliers" but Arima model orders (p,d,q)(P,D,Q) can also be re-identified;

8. Complete: All the parameters are re-identified and re-estimated, unless constrained in the domain spec.

Value

a new specification, an object of class "JD3_X13_SPEC" or "JD3_REGARIMA_SPEC".

References

More information on revision policies in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/a-rev-policies

Examples

y <- rjd3toolkit::ABS$X0.2.08.10.M

# raw series for first estimation
y_raw <- window(y, end = c(2016, 12))

# raw series for second (refreshed) estimation
y_new <- window(y, end = c(2017, 6))

# specification for first estimation
spec_x13_1 <- x13_spec("rsa5c")

# first estimation
sa_x13 <- x13(y_raw, spec_x13_1)

# refreshing the specification
current_result_spec <- sa_x13$result_spec
current_domain_spec <- sa_x13$estimation_spec

# policy = "Fixed"
spec_x13_ref <- x13_refresh(current_result_spec, # point spec to be refreshed
    current_domain_spec, # domain spec (set of constraints)
    policy = "Fixed"
)
# 2nd estimation with refreshed specification
sa_x13_ref <- x13(y_new, spec_x13_ref)

# policy = "Outliers"
spec_x13_ref <- x13_refresh(current_result_spec,
    current_domain_spec,
    policy = "Outliers",
    period = 12,
    start = c(2017, 1)
) # outliers will be re-detected from January 2017 included
# 2nd estimation with refreshed specification
sa_x13_ref <- x13(y_new, spec_x13_ref)

# policy = "Current"
spec_x13_ref <- x13_refresh(current_result_spec,
    current_domain_spec,
    policy = "Current",
    period = 12,
    start = c(2017, 1),
    end = end(y_new)
)

# Points from January 2017 (included) until the end of the series will be
# treated as Additive Outliers, the previous reg-Arima model being otherwise
# kept fixed 2nd estimation with refreshed specification
sa_x13_ref <- x13(y_new, spec_x13_ref)

# Same procedure using regarima_refresh
# specification for first estimation
spec_1 <- regarima_spec("rg3")

# First estimation
reg_a_model <- regarima(y_raw, spec_1)
reg_a_model$estimation_spec

# Refreshing the specification
current_result_spec <- reg_a_model$result_spec
current_domain_spec <- reg_a_model$estimation_spec

# Policy = "Fixed"
spec_1_ref <- regarima_refresh(
    current_result_spec, # point spec to be refreshed
    current_domain_spec, # domain spec (set of constraints)
    policy = "Fixed"
)
# 2nd estimation with refreshed specification
reg_a_model_ref <- regarima(y_new, spec_1_ref)

RegARIMA model, pre-adjustment in X13

Description

RegARIMA model, pre-adjustment in X13

Usage

regarima(
  ts,
  spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"),
  context = NULL,
  userdefined = NULL
)

regarima_fast(
  ts,
  spec = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"),
  context = NULL,
  userdefined = NULL
)

Arguments

Value

the regarima() function returns a list with the results ("JD3_REGARIMA_RSLTS" object), the estimation specification and the result specification, while regarima_fast() is a faster function that only returns the results.

Examples

library("rjd3toolkit")

y <- rjd3toolkit::ABS$X0.2.09.10.M
sp <- regarima_spec("rg5c")
sp <- add_outlier(sp,
    type = c("AO"), c("2015-01-01", "2010-01-01")
)
regarima_fast(y, spec = sp)
sp <- set_transform(
    set_tradingdays(
        set_easter(sp, enabled = FALSE),
        option = "workingdays"
    ),
    fun = "None"
)
regarima_fast(y, spec = sp)
sp <- set_outlier(sp, outliers.type = c("AO"))
regarima_fast(y, spec = sp)

Outlier Detection with a RegARIMA Model

Description

Outlier Detection with a RegARIMA Model

Usage

regarima_outliers(
  y,
  order = c(0L, 1L, 1L),
  seasonal = c(0L, 1L, 1L),
  mean = FALSE,
  X = NULL,
  X.td = NULL,
  ao = TRUE,
  ls = TRUE,
  tc = FALSE,
  so = FALSE,
  cv = 0,
  clean = FALSE
)

Arguments

Value

a "JD3_REGARIMA_OUTLIERS" object, containing input variables and results

Examples

# estimate model
model <- regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
# print outliers
model$model$variables

Set X-11 Specification

Description

Set X-11 Specification

Usage

set_x11(
  x,
  mode = c(NA, "Undefined", "Additive", "Multiplicative", "LogAdditive",
    "PseudoAdditive"),
  seasonal.comp = NA,
  seasonal.filter = NA,
  henderson.filter = NA,
  lsigma = NA,
  usigma = NA,
  fcasts = NA,
  bcasts = NA,
  calendar.sigma = c(NA, "None", "Signif", "All", "Select"),
  sigma.vector = NA,
  exclude.forecast = NA,
  bias = c(NA, "LEGACY")
)

Arguments

Value

a "JD3_X11_SPEC" object, containing all the parameters.

See Also

x13_spec() and x11_spec().

Examples

init_spec <- x11_spec()
new_spec <- set_x11(init_spec,
    mode = "LogAdditive",
    seasonal.comp = 1,
    seasonal.filter = "S3X9",
    henderson.filter = 7,
    lsigma = 1.7,
    usigma = 2.7,
    fcasts = -1,
    bcasts = -1,
    calendar.sigma = "All",
    sigma.vector = NA,
    exclude.forecast = FALSE,
    bias = "LEGACY"
)

X-11 Decomposition Algorithm

Description

X-11 Decomposition Algorithm

Usage

x11(ts, spec = x11_spec(), userdefined = NULL)

Arguments

Value

the x11() function returns a list with the results (series) and final parameters

Examples

y <- rjd3toolkit::ABS$X0.2.09.10.M
x11_spec <- x11_spec()
x11(y, x11_spec)
x11_spec <- set_x11(x11_spec, henderson.filter = 13)
x11(y, x11_spec)

Seasonal Adjustment with X13-ARIMA

Description

Seasonal Adjustment with X13-ARIMA

Usage

x13(
  ts,
  spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
  context = NULL,
  userdefined = NULL
)

x13_fast(
  ts,
  spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
  context = NULL,
  userdefined = NULL
)

jx13(
  ts,
  spec = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"),
  context = NULL,
  userdefined = NULL
)

Arguments

Value

the x13() function returns a list with the results, the estimation specification and the result specification, while x13_fast() is a faster function that only returns the results. The jx13() functions only returns results in a java object which will allow to customize outputs in other packages (use rjd3toolkit::dictionary() to get the list of variables and rjd3toolkit::result() to get a specific variable). In the estimation functions x13() and x13_fast() you can directly use a specification name (string). If you want to customize a specification you have to create a specification object first.

Examples

library("rjd3toolkit")

y <- rjd3toolkit::ABS$X0.2.09.10.M
x13_fast(y, "rsa3")
x13(y, "rsa5c")
regarima_fast(y, "rg0")
regarima(y, "rg3")

sp <- x13_spec("rsa5c")
sp <- add_outlier(sp,
    type = c("AO"), c("2015-01-01", "2010-01-01")
)
sp <- set_transform(
    set_tradingdays(
        set_easter(sp, enabled = FALSE),
        option = "workingdays"
    ),
    fun = "None"
)
x13(y, spec = sp)
sp <- set_x11(sp,
    henderson.filter = 13
)
x13_fast(y, spec = sp)
j <- jx13(y, spec = sp)
class(j)

X-13 Dictionary

Description

Functions to provide information for all output objects (series, diagnostics, parameters) available with x13() function.

Usage

x13_dictionary()

x13_full_dictionary()

Details

These functions provide lists of output names (series, diagnostics, parameters) available with the x13() function. These names can be used to generate customized outputs with the userdefined option of the x13() function (see examples). The x13_full_dictionary function provides additional information on object format and description.

Value

x13_dictionary() returns a character vector containing the names of all output objects (series, diagnostics, parameters) available with the x13() function, whereas x13_full_dictionary() returns a data.frame with format and description, for all the output objects.

Examples

# Visualize the dictionary
print(x13_dictionary())
summary(x13_dictionary())

# first 10 lines
head(x13_full_dictionary(), n = 10)
# For more structured information call `View(x13_full_dictionary())`

# Extract names of output of interest
user_defined_output <- x13_dictionary()[c(65, 95, 135)]
user_defined_output

# Generate the corresponding output in an estimation
y <- rjd3toolkit::ABS$X0.2.09.10.M
m <- x13(y,"rsa3", userdefined=user_defined_output)

# Retrieve user defined output
tail(m$user_defined$ylin)
m$user_defined$residuals.kurtosis
m$user_defined$sa_f

Revisions History

Description

Compute revisions history

Usage

x13_revisions(
  ts,
  spec,
  data_ids = NULL,
  ts_ids = NULL,
  cmp_ids = NULL,
  context = NULL
)

Arguments

Value

returns a list

Examples

s <- rjd3toolkit::ABS$X0.2.09.10.M
sa_mod <- x13(s)
data_ids <- list(
    # Get the coefficient of the trading-day coefficient from 2005-jan
    list(start = "2005-01-01", id = "regression.td(1)"),
    # Get the ljung-box statistics on residuals from 2010-jan
    list(start = "2010-01-01", id = "residuals.lb")
)
ts_ids <- list(
    # Get the SA component estimates of 2010-jan from 2010-jan
    list(period = "2010-01-01", start = "2010-01-01", id = "sa"),
    # Get the irregular component estimates of 2010-jan from 2015-jan
    list(period = "2010-01-01", start = "2015-01-01", id = "i")
)
cmp_ids <- list(
    # Get the SA component estimates (full time series) 2010-jan to 2020-jan
    list(start = "2010-01-01", end = "2020-01-01", id = "sa"),
    # Get the trend component estimates (full time series)  2010-jan to 2020-jan
    list(start = "2010-01-01", end = "2020-01-01", id = "t")
)
rh <- x13_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)
rh$data
rh$series
rh$components

RegARIMA/X-13 Default Specifications

Description

Set of functions to create default specification objects associated with the X-13ARIMA seasonal adjustment method.

Specification setting of sheer X-11 decomposition method (without reg-arima pre-adjustment) is supported by the x11_spec() function only and doesn't appear among the possible X13-Arima default specifications.

Specification setting can be restricted to the reg-arima part with the regarima_spec() function, without argument regarima_spec() yields a RG5c specification.

When setting a complete X13-Arima spec, x13_spec() without argument yields a RSA5c specification.

Usage

regarima_spec(name = c("rg4", "rg0", "rg1", "rg2c", "rg3", "rg5c"))

x13_spec(name = c("rsa4", "rsa0", "rsa1", "rsa2c", "rsa3", "rsa5c"))

x11_spec()

Arguments

Details

The available predefined 'JDemetra+' model specifications are described in the table below:

Value

an object of class "JD3_X13_SPEC" (x13_spec()), "JD3_REGARIMA_SPEC" (regarima_spec()) or "JD3_X11_SPEC" (x11_spec()).

See Also

• To set the pre-processing parameters: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar().

• To set the decomposition parameters: set_x11().

• To set the benchmarking parameters: rjd3toolkit::set_benchmarking().

Examples

init_spec <- x11_spec()
init_spec
init_spec <- regarima_spec("rg4")
init_spec
init_spec <- x13_spec("rsa5c")
init_spec