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etasFLP (version 2.2.2)

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

Description

Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017).

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Version

Install

install.packages('etasFLP')

Monthly Downloads

198

Version

2.2.2

License

GPL (>= 2)

Maintainer

Marcello Chiodi

Last Published

September 14th, 2023

Functions in etasFLP (2.2.2)

compare.etasclass

Compare two etasclass objects
etas.starting

Guess starting values of ETAS parameters (beta-version). Only from package version 1.2.0
californiacatalog

Sample catalog of North California earthquakes
eqcat

Check earthquake catalog
profile.etasclass

profile method for etasclass objects (ETAS model) (To be checked)
MLA.freq

Display a pretty frequency table
etasFLP-package

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

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

Plot method for etasclass objects
etasclass

Mixed estimation of an ETAS model (renewed in version 2.0)
simpson.coeff

Computes Simpson integration rule coefficients
plot.profile.etasclass

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

timeupdate.etasclass
xy.grid

Creates a 2-d grid
etasFLP-internal

Internal etasFLP functions
print.etasclass

Print method for etasclass objects
italycatalog

Small sample catalog of italian earthquakes
update.etasclass

update.etasclass
kde2dnew.fortran

A 2-d normal kernel estimator
summary.etasclass

Summary method for etasclass objects
time2date

Date time conversion tools
daily.etasclass

Title daily.etasclass
catalog.withcov

Small sample catalog of italian earthquakes with covariates
bwd.nrd

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

Estimates the parameter of the Gutenberg-Richter law.