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hpa (version 1.0.1)

Distributions Hermite Polynomial Approximation

Description

Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) .

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Version

Install

install.packages('hpa')

Monthly Downloads

459

Version

1.0.1

License

GPL-3

Maintainer

Potanin Bogdan

Last Published

August 25th, 2019

Functions in hpa (1.0.1)

AIC.hpaML

Calculates AIC for "hpaML" object
etrhpa

Expected powered product hermite polynomial approximation for truncated distribution
AIC.hpaSelection

Calculates AIC for "hpaSelection" object
AIC_hpaSelection

Calculates AIC for "hpaSelection" object
AIC_hpaML

Calculates AIC for "hpaML" object
ehpa

Expected powered product hermite polynomial approximation
dhpa

Density function hermite polynomial approximation
AIC.hpaBinary

Calculates AIC for "hpaBinary" object
AIC_hpaBinary

Calculates AIC for "hpaBinary" object
dtrhpa

Truncated density function hermite polynomial approximation
hpa-package

Provides Tools for Multivariate Distributions Hermite Polynomial Approximation
print.summary.hpaBinary

Summary for hpaBinary output
plot_hpaSelection

Plot hpaSelection random errors approximated density
predict_hpaSelection

Predict outcome and selection equation values from hpaSelection model
plot_hpaBinary

Plot hpaBinary random errors approximated density
logLik.hpaSelection

Calculates log-likelihood for "hpaSelection" object
phpa

Distribution function hermite polynomial approximation
normalMoment

Calculate k-th order moment of normal distribution
logLik_hpaBinary

Calculates log-likelihood for "hpaBinary" object
printPolynomial

Print polynomial given it's degrees and coefficients
logLik.hpaBinary

Calculates log-likelihood for "hpaBinary" object
hpaML

Semi-nonparametric maximum likelihood estimation
print_summary_hpaBinary

Summary for hpaBinary output
print_summary_hpaML

Summary for hpaML output
hpaBinary

Perform semi-nonparametric binary choice model estimation
print_summary_hpaSelection

Summary for hpaSelection output
summary.hpaML

Summarizing hpaML Fits
itrhpa

Truncated interval distribution function hermite polynomial approximation for truncated distribution
ihpa

Interval distribution function hermite polynomial approximation
plot.hpaSelection

Plot hpaSelection random errors approximated density
summary.hpaBinary

Summarizing hpaBinary Fits
plot.hpaBinary

Plot hpaBinary random errors approximated density
hpaSelection

Perform semi-nonparametric selection model estimation
polynomialIndex

Returns matrix of polynomial indexes
predict.hpaBinary

Predict method for hpaBinary
print.summary.hpaML

Summary for hpaML output
print.summary.hpaSelection

Summary for hpaSelection output
logLik.hpaML

Calculates log-likelihood for "hpaML" object
logLik_hpaML

Calculates log-likelihood for "hpaML" object
predict.hpaSelection

Predict outcome and selection equation values from hpaSelection model
summary_hpaSelection

Summarizing hpaSelection Fits
summary_hpaML

Summarizing hpaML Fits
predict.hpaML

Predict method for hpaML
summary.hpaSelection

Summarizing hpaSelection Fits
summary_hpaBinary

Summarizing hpaBinary Fits
predict_hpaBinary

Predict method for hpaBinary
truncatedNormalMoment

Calculate k-th order moment of truncated normal distribution
predict_hpaML

Predict method for hpaML
logLik_hpaSelection

Calculates log-likelihood for "hpaSelection" object