Interface to 'JDemetra+' 3.x (<https://github.com/jdemetra>)
    time series analysis software.  It offers full access to options and
    outputs of 'TRAMO-SEATS' (Time series Regression with ARIMA noise,
    Missing values and Outliers - Signal Extraction in ARIMA Time Series),
    including 'TRAMO' modelling (ARIMA model with outlier detection and
    trading days adjustment). ARIMA = AutoRegressive Integrated Moving Average.

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.

The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.

Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially
    recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.
    It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods
    TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.

Functions to have nice 'rmarkdown' outputs of the 
  seasonal and trading day adjustment models made with 'RJDemetra'.

Add-in to the 'RJDemetra' package on seasonal adjustments.
    It allows to produce dashboards to summarise models and quickly check the quality of the seasonal adjustment.

Set of tools to manipulate the 'JDemetra+' workspaces.  Based
    on the 'RJDemetra' package (which interfaces with version 2 of the
    'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal
    adjustment software officially recommended to the members of the
    European Statistical System (ESS) and the European System of Central
    Banks).  This package provides access to additional workspace
    manipulation functions such as metadata manipulation, raw paths and
    wrangling of several workspaces simultaneously.  These additional
    functionalities are useful as part of a CVS data production chain.