Functions that support estimating, assessing and mapping regional
disaggregated indicators. So far, estimation methods comprise direct estimation,
the model-based unit-level approach Empirical Best Prediction (see "Small area
estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>),
the area-level model (see "Estimates of income for small places: An
application of James-Stein procedures to Census Data" by Fay and Herriot (1979)
<doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance
estimation methods, log and arcsin transformation, spatial, robust and measurement
error models), as well as their precision estimates. The assessment of the used model
is supported by a summary and diagnostic plots. For a suitable presentation of
estimates, map plots can be easily created. Furthermore, results can easily be
exported to excel. For a detailed description of the package and the methods used
see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators"
by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette
"A Framework for Producing Small Area Estimates Based on Area-Level Models in R".

Simon Urbanek <urbanek@research.att.com>, Yossi Rubner
Package providing calculation of Earth Mover's Distance
(EMD).

Package providing calculation of Earth Mover's Distance (EMD).

Exponentially Modified Gaussian (EMG) Distribution
Provides basic distribution functions for a mixture model
of a gaussian and exponential distribution.

Provides basic distribution functions for a mixture model of a gaussian and exponential distribution.

Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.

person("Kyungsub", "Lee", email = "kyungsub@gmail.com", role = c("aut", "cre"))
Simulate and fitting exponential multivariate Hawkes model.
This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.1093/biomet/58.1.83>, with an exponential kernel and fits the parameters from the data.
Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated.
The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with 'maxLik' package.
'harrival.R' 'hspec.R' 'hmoment.R' 'hllf.R' 'hfit.R'
'utilities.R' 'hreal.R' 'hsim.R' 'script.R'