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etasFLP (version 1.1.1)

Mixed FLP and ML Estimation of ETAS Space-Time Point Processes

Description

Estimation of the components of an ETAS model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood. The two estimation steps are alternated until convergence is obtained. For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present. Some descriptive functions for earthquakes catalogs are present; also plot, print, summary, profile methods are defined for main output (objects of class "etasclass").

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Version

Install

install.packages('etasFLP')

Monthly Downloads

198

Version

1.1.1

License

GPL (>= 2)

Maintainer

Marcello Chiodi

Last Published

October 30th, 2014

Functions in etasFLP (1.1.1)

MLA.freq

Display a pretty frequency table
etasFLP-internal

Internal etasFLP functions
magn.plot

Transformed plot of the magnitudes distribution of an earthquakes catalog
plot.profile.etasclass

plot method for profile.etasclass objects (profile likelihood of ETAS model)
simpson.coeff

Computes Simpson integration rule coefficients
xy.grid

Creates a 2-d grid
time2date

Date time conversion tools
plot.etasclass

Plot method for etasclass objects
compare.etasclass

Compare two etasclass objects
kde2dnew.fortran

A 2-d normal kernel estimator
italycatalog

Small sample catalog of italian earthquakes
californiacatalog

Sample catalog of North California earthquakes
eqcat

Check earthquake catalog
profile.etasclass

profile method for etasclass objects (ETAS model)
summary.etasclass

Summary method for etasclass objects
print.etasclass

Print method for etasclass objects
etasclass

Mixed estimation of an ETAS model
etasFLP-package

Mixed FLP and ML Estimation of ETAS Space-Time Point Processes
b.guten

Estimates the parameter of the Gutenberg-Richter law.
bwd.nrd

Silverman's rule optimal for the estimation of a kernel bandwidth