Package index • rjd3toolkit

Customizing specifications

Functions allowing to set user defined parameters in X-13ARIMA (rjd3x13) or TRAMO-SEATS (rjd3tramoseats)

add_outlier() remove_outlier() add_ramp() remove_ramp(): Manage Outliers/Ramps in Specification

add_usrdefvar(): Add a User-Defined Variable to Pre-Processing Specification.

modelling_context(): Create modelling context

set_arima(): Set ARIMA Model Structure in Pre-Processing Specification

set_automodel(): Set Arima Model Identification in Pre-Processing Specification

set_basic(): Set estimation sub-span and quality check specification

set_benchmarking(): Set Benchmarking Specification

set_easter(): Set Easter effect correction in Pre-Processing Specification

set_estimate(): Set Numeric Estimation Parameters and Modelling Span

set_outlier(): Set Outlier Detection Parameters

set_tradingdays(): Set Calendar effects correction in Pre-Processing Specification

set_transform(): Set Log-level Transformation and Decomposition scheme in Pre-Processing Specification

Calendars

Functions allowing to define customized calendars

easter_dates(): Display Easter Sunday dates in given period

easter_day(): Set a Holiday on an Easter related day

fixed_day(): Set a holiday on a Fixed Day

fixed_week_day(): Set a Holiday on a Fixed Week Day

single_day(): Set a holiday on a Single Day

special_day(): List of Pre-Defined Holidays to choose from

national_calendar(): Create a National Calendar

chained_calendar(): Create a Chained Calendar

weighted_calendar(): Create a Composite Calendar

Calendar Regressors

Functions allowing to generate calendar regressors

calendar_td(): Trading day regressors with pre-defined holidays

easter_variable() julianeaster_variable(): Easter regressor

holidays(): Daily calendar regressors corresponding to holidays

lp_variable(): Leap Year regressor

long_term_mean(): Display Long-term means for a set of calendar regressors

stock_td(): Trading day Regressor for Stock series

Outliers, Intervention variables and Ramps

Functions allowing to generate outliers, intervention variables and ramps

ao_variable() tc_variable() ls_variable() so_variable(): Generating Outlier regressors

intervention_variable(): Intervention variable

ramp_variable(): Ramp regressor

periodic_dummies() periodic_contrasts(): Periodic dummies and contrasts

trigonometric_variables(): Trigonometric variables

Seasonality Tests

Functions to test the presence of seasonality in a time series

seasonality_canovahansen(): Canova-Hansen seasonality test

seasonality_canovahansen_trigs(): Canova-Hansen test using trigonometric variables

seasonality_combined(): "X12" Test On Seasonality

seasonality_f(): F-test on seasonal dummies

seasonality_friedman(): Friedman Seasonality Test

seasonality_kruskalwallis(): Kruskall-Wallis Seasonality Test

seasonality_modified_qs(): Modified QS Seasonality Test (Maravall)

seasonality_periodogram(): Periodogram Seasonality Test

seasonality_qs(): QS (seasonal Ljung-Box) test.

Residual Trading Days Tests

Functions to test the presence of residual trading days effects

td(): Trading day regressors without holidays

td_canovahansen(): Canova-Hansen test for stable trading days

td_f(): Residual Trading Days Test

td_timevarying(): Likelihood ratio test on time varying trading days

Tests of Independence and Normality

Functions allowing to test if residuals are White Noise

bowmanshenton() doornikhansen() jarquebera() skewness() kurtosis(): Normality Tests

ljungbox(): Ljung-Box Test

Randomness Tests

Functions to test data randomness

testofruns() testofupdownruns(): Runs Tests around the mean or the median

(S)arima Models

Functions to wrangle (S)arima models

arima_difference(): Remove an arima model from an existing one.

arima_model(): ARIMA Model

arima_properties(): Properties of an ARIMA model

arima_sum(): Sum ARIMA Models

sarima_decompose(): Decompose SARIMA Model into three components trend, seasonal, irregular

sarima_estimate(): Estimate SARIMA Model

sarima_hannan_rissanen(): Estimate ARIMA Model with Hannan-Rissanen method

sarima_model(): Seasonal ARIMA model (Box-Jenkins)

sarima_properties(): SARIMA Properties

sarima_random(): Simulate Seasonal ARIMA

UC Decomposition

Functions to perform canonical decomposition of a Sarima Model

ucarima_canonical(): Makes a UCARIMA model canonical; more specifically, put all the noise of the components in one dedicated component

ucarima_estimate(): Estimate UCARIMA Model

ucarima_model(): Creates an UCARIMA model, which is composed of ARIMA models with independent innovations.

ucarima_wk(): Wiener Kolmogorov Estimators

Data (TS) Transformations

Functions to compute basic transformations on time series

aggregate(): Aggregation of time series

clean_extremities(): Removal of missing values at the beginning/end

daysOf(): Provides a list of dates corresponding to each period of the given time series

differences(): Differencing of a series

differencing_fast(): The series is differenced till its variance is decreasing.

do_stationary(): Automatic stationary transformation

ts_adjust(): Multiplicative adjustment of a time series for leap year / length of periods

ts_interpolate(): Interpolation of a time series with missing values

tsdata_of(): Title

Autocorrelations

Functions allowing to compute autocorrelations

autocorrelations() autocorrelations_partial() autocorrelations_inverse(): Autocorrelation Functions

Statistics and regressions

compare_annual_totals(): Compare the annual totals of two series

mad(): Compute a robust median absolute deviation (MAD)

rangemean_tstat(): Range-Mean Regression

Distributions

PDFs, CDFs and inverses

density_chi2() cdf_chi2() random_chi2(): The Chi-Squared Distribution

density_gamma() cdf_gamma() random_gamma(): The Gamma Distribution

density_inverse_gamma() cdf_inverse_gamma() random_inverse_gamma(): The Inverse-Gamma Distribution

density_inverse_gaussian() cdf_inverse_gaussian() random_inverse_gaussian(): The Inverse-Gaussian Distribution

density_t() cdf_t() random_t(): The Student Distribution

Splines

Functions to generate different types of splines

bsplines(): B-Splines

periodic_bsplines(): Periodic B-Splines

periodic_cspline(): Periodic cubic spline

periodic_csplines(): Periodic cardinal cubic splines

monotonic_cspline(): Monotonic cubic spline

natural_cspline(): Natural cubic spline

(Output) Dictionary

Functions allowing to get objects names and contents

dictionary() result() user_defined(): Get Dictionary and Result

reload_dictionaries(): Reload dictionaries

Databases

Lists of Time Series from ABS, NBB and US Census Bureau

ABS: Retail trade statistics in Australia

Exports: Belgian exports to European countries

Imports: Belgian imports from European countries

Retail: US Retail trade statistics

Print and Plot Methods

Functions to print and plot specific JDemetra+ class objects

diagnostics(): Generic Diagnostics Function

sadecomposition() print(<JD3_SADECOMPOSITION>) plot(<JD3_SADECOMPOSITION>) sa_decomposition(): Generic Function for Seasonal Adjustment Decomposition

sa_preprocessing(): Generic Preprocessing Function

statisticaltest() print(<JD3_TEST>): Generic Function For 'JDemetra+' Tests

Full JDemetra+ TS objects

Creating Full JDemetra+ TS objects

data_to_ts(): Promote a R time series to a "full JDemetra+ time series"

to_ts(): Creates a time series object

to_tscollection(): Creates a collection of time series

Wrangling Java objects

Functions to easily interact between R and Java objects

.r2jd_tsdata() .r2jd_tsdomain() .jd2r_tsdata() .jd2r_mts() .jd2r_lts() .jd2r_matrix() .r2jd_matrix() .jdomain() .enum_sextract() .enum_sof() .enum_extract() .enum_of() .r2p_parameter() .p2r_parameter() .r2p_parameters() .r2p_lparameters() .p2r_parameters() .p2r_parameters_rslt() .p2r_parameters_rsltx() .p2r_test() .p2r_matrix() .p2r_tsdata() .r2p_tsdata() .p2r_parameters_estimation() .p2r_likelihood() .p2r_date() .r2p_date() .p2r_span() .r2p_span() .p2r_arima() .p2r_ucarima() .p2r_spec_sarima() .r2p_spec_sarima() .p2r_outliers() .r2p_outliers() .p2r_sequences() .r2p_sequences() .p2r_iv() .r2p_iv() .p2r_ivs() .r2p_ivs() .p2r_ramps() .r2p_ramps() .p2r_uservars() .r2p_uservars() .p2r_variables() .p2r_sa_decomposition() .p2r_sa_diagnostics() .p2r_spec_benchmarking() .r2p_spec_benchmarking() .r2jd_sarima() .jd2r_ucarima() .p2jd_calendar() .r2p_calendar() .proc_numeric() .proc_vector() .proc_int() .proc_bool() .proc_ts() .proc_str() .proc_desc() .proc_test() .proc_parameter() .proc_parameters() .proc_matrix() .proc_data() .proc_dictionary() .proc_dictionary2() .proc_likelihood() .r2p_moniker() .p2r_moniker() .r2p_datasupplier() .p2r_metadata() .r2p_metadata() .p2r_ts() .r2p_ts() .p2r_tscollection() .r2p_tscollection() .r2jd_ts() .jd2r_ts() .r2jd_tscollection() .jd2r_tscollection() .p2r_datasupplier() .r2p_datasuppliers() .p2r_datasuppliers() .p2jd_variables() .jd2p_variables() .jd2r_variables() .r2jd_variables() .p2r_context() .r2p_context() .p2jd_context() .jd2p_context() .jd2r_modellingcontext() .r2jd_modellingcontext() .p2r_calendars() .r2p_calendars() .p2jd_calendars() .jd2p_calendars() .jd2r_calendars() .r2jd_calendars() .jd3_object() .p2r_regarima_rslts() .r2jd_tmp_ts() .r2jd_make_ts() .r2jd_make_tscollection() DATE_MIN DATE_MAX: Java Utility Functions

print(<JD3_ARIMA>) print(<JD3_UCARIMA>) print(<JD3_SARIMA>) print(<JD3_SARIMA_ESTIMATION>) print(<JD3_SPAN>) print(<JD3_LIKELIHOOD>) print(<JD3_REGARIMA_RSLTS>): JD3 print functions

print(<JD3_FIXEDDAY>) print(<JD3_FIXEDWEEKDAY>) print(<JD3_EASTERDAY>) print(<JD3_SPECIALDAY>) print(<JD3_SINGLEDAY>) print(<JD3_CALENDAR>): Calendars Print Methods

r2jd_calendarts(): Create Java CalendarTimeSeries

.add_ud_var(): Add user-defined variable to a SA model

.likelihood(): Information on the (log-)likelihood

.tsmoniker(): Create a Moniker

Deprecated functions

Avoid Using

sa.decomposition(): Deprecated functions