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hpa (version 1.1.2)

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

1.1.2

License

GPL-3

Maintainer

Potanin Bogdan

Last Published

August 24th, 2020

Functions in hpa (1.1.2)

AIC_hpaSelection

Calculates AIC for "hpaSelection" object
dhpaDiff

Calculate gradient of density function hermite polynomial approximation
dhpa

Density function hermite polynomial approximation
AIC_hpaML

Calculates AIC for "hpaML" object
AIC.hpaBinary

Calculates AIC for "hpaBinary" object
AIC.hpaML

Calculates AIC for "hpaML" object
AIC.hpaSelection

Calculates AIC for "hpaSelection" object
AIC_hpaBinary

Calculates AIC for "hpaBinary" object
dnorm_parallel

Calculate normal pdf in parallel
dtrhpa

Truncated density function hermite polynomial approximation
hpaSelection

Perform semi-nonparametric selection model estimation
etrhpa

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

Calculates log-likelihood for "hpaSelection" object
logLik_hpaBinary

Calculates log-likelihood for "hpaBinary" object
ehpa

Expected powered product hermite polynomial approximation
plot_hpaBinary

Plot hpaBinary random errors approximated density
logLik.hpaBinary

Calculates log-likelihood for "hpaBinary" object
logLik_hpaSelection

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

Calculates log-likelihood for "hpaML" object
logLik_hpaML

Calculates log-likelihood for "hpaML" object
ihpaDiff

Calculate gradient of interval distribution function hermite polynomial approximation
itrhpa

Truncated interval distribution function hermite polynomial approximation for truncated distribution
predict.hpaBinary

Predict method for hpaBinary
normalMoment

Calculate k-th order moment of normal distribution
mecdf

Calculates multivariate empirical cumulative distribution function
predict_hpaML

Predict method for hpaML
predict_hpaSelection

Predict outcome and selection equation values from hpaSelection model
print.summary.hpaML

Summary for hpaML output
predict_hpaBinary

Predict method for hpaBinary
predict.hpaSelection

Predict outcome and selection equation values from hpaSelection model
print.summary.hpaSelection

Summary for "hpaSelection" object
print.summary.hpaBinary

Summary for "hpaBinary" object
print.hpaSelection

Print method for "hpaSelection" object
predict.hpaML

Predict method for hpaML
plot_hpaSelection

Plot hpaSelection random errors approximated density
summary_hpaSelection

Summarizing hpaSelection Fits
summary_hpaML

Summarizing hpaML Fits
hpaBinary

Perform semi-nonparametric binary choice model estimation
print_summary_hpaSelection

Summary for hpaSelection output
print_summary_hpaML

Summary for hpaML output
summary.hpaBinary

Summarizing hpaBinary Fits
summary.hpaML

Summarizing hpaML Fits
ihpa

Interval distribution function hermite polynomial approximation
plot.hpaML

Plot approximated marginal density using hpaML output
pnorm_parallel

Calculate normal cdf in parallel
plot.hpaSelection

Plot hpaSelection random errors approximated density
polynomialIndex

Returns matrix of polynomial indexes
phpa

Distribution function hermite polynomial approximation
printPolynomial

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

Plot hpaBinary random errors approximated density
hpaML

Semi-nonparametric maximum likelihood estimation
print_summary_hpaBinary

Summary for hpaBinary output
summary.hpaSelection

Summarizing hpaSelection Fits
print.hpaBinary

Print method for "hpaBinary" object
truncatedNormalMoment

Calculate k-th order moment of truncated normal distribution
print.hpaML

Print method for "hpaML" object
summary_hpaBinary

Summarizing hpaBinary Fits