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.

A series of functions in some way considered useful to the author.  These 
    include methods for subsetting tables and generating indices for arrays, 
    conditioning and intervening in probability distributions, generating 
    combinations, fast transformations, and more...

Performs reversible-jump Markov chain Monte Carlo (Green, 1995)
    <doi:10.2307/2337340>, specifically the restriction introduced by 
    Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising 
    a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling 
    problem. Previously-calculated posterior distributions are used to 
    quickly estimate posterior model probabilities. Jacobian matrices are 
    found using automatic differentiation. For a detailed description of
    the package, see Gelling, Schofield & Barker (2019)
    <doi:10.1111/anzs.12263>.

Implementation of the Johnson Quantile-Parameterised Distribution in R. 
  The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution
  system that is parameterised by a symmetric percentile triplet of quantile values
  (typically the 10th-50th-90th) along with known support bounds for the distribution.
  The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>.
  This package implements the density, quantile, CDF and random number generator functions.

Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: <https://sdmx.org>.