This man page documents some major changings in RandomFields.
full (univariate) trend modelling
error in particular in
RFfit runs much faster now
modus operandi changed for estimating
log Gauss field corrected (has been a log log Gauss field)
RMconstant is now called
Conditional simulation: several severe typos corrected.
RandomFields is now based on S4 objects using the package sp. The functions accept both sp objects and simple objects as used in version 2. See also above.
each model has now its own man page;
classes of models and functions are bundled in several pages:
Covariance models start with
RM, distribution families
RR, processes with
the man pages of several functions are split into two parts:
(i) a beginners man page which includes a link to
(ii) man pages for advanced users
The interfaces become simpler, at the same time more powerful
than the functions in version 2. E.g.,
RFsimulate can perform
unconditional simulation, conditional simulation and random
Only those arguments are kept in the functions that are considered as being absolutely necessary. All the other arguments can be included as options.
RFgui is an instructive interface based on tcl/tk,
replacing the former
Inference for Gaussian random fields
RFfit has undergone a major revision. E.g.:
(i) estimation of random effect models with spatial covariance structure
(ii) automatic estimation of 10 and more arguments in multivariate and/or space-time models
RFvariogram is now based on an fft algorithm
if the data are on a grid, even allowing for missing values.
RFratiotest has been added.
Modelling of maxstable processes has been enhanced, including
(i) the simulation of Brown-Resnick processes
(ii) initial support of tail correlation functions;
Further processes chi2 processes, compound Poisson processes, binary processes added.
the formula notation for linear models may now be defined
Novel, user friendly definition of the covariance models
Multivariate and vector-valued random fields are now fully included
The user may now define his own functions, to some extend.
The trend allows for much more flexibility
Distributions may now be included which will be extended to Bayesian modelling in future.
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