Title: Building Kriging Models from FANOVA Graphs
Description: Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the RStudio IDE with RStudio's package 'manipulate' loaded. RStudio is freely available (www.rstudio.org), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only.

Author: Jana Fruth, Thomas Muehlenstaedt, Olivier Roustant, Malte Jastrow, Sonja Kuhnt
Description: Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the 'RStudio IDE' with 'RStudio' package 'manipulate' loaded. 'RStudio' is freely available (<https://rstudio.com/>), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only. For further information on the method see Fruth, J., Roustant, O., Kuhnt, S. (2014) <doi:10.1016/j.jspi.2013.11.007>.

Title: Visualisations of sequential probability distributions.
Maintainer: Guy J. Abel <g.j.abel@gmail.com>
Description: The fanplot package contains a collection of R functions
to effectively display plots of probabilistic forecasts, and
other types of distributions, that may develop along an x-axis
of a plot. The plotting of these distributions is based around
two functions. The first function, pn, calculates the
percentiles over a time period or form of index represented on
an x-axis for a given distribution of results. These
distributions typically come from some form of simulation. The
second function, fan, plots the calculated percentiles of the
distributions. The fanplot package contains some example MCMC
simulations from stochastic volatility models to help

to effectively display plots of sequential distributions such
as probabilistic forecasts. The plotting of distributions are
based around two functions. The first, pn, calculates the
percentiles for a set of sequential distributions over a
specified time period. The second, fan, plots the calculated
percentiles of the sequential distributions. The resulting plot
is a set of coloured polygon, with shadings corresponding to