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hpa (version 1.1.3)

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

1.1.3

License

GPL-3

Maintainer

Potanin Bogdan

Last Published

November 17th, 2020

Functions in hpa (1.1.3)

pnorm_parallel

Calculate normal cdf in parallel
polynomialIndex

Multivariate Polynomial Representation
predict.hpaBinary

Predict method for hpaBinary
logLik.hpaML

Calculates log-likelihood for "hpaML" object
hpaML

Semi-nonparametric maximum likelihood estimation
hpaDist

Probabilities and Moments Hermite Polynomial Approximation
logLik_hpaML

Calculates log-likelihood for "hpaML" object
logLik_hpaBinary

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

Calculates log-likelihood for "hpaSelection" object
logLik_hpaSelection

Calculates log-likelihood for "hpaSelection" object
plot_hpaBinary

Plot hpaBinary random errors approximated density
hpaBinary

Semi-nonparametric single index binary choice model estimation
plot.hpaBinary

Plot hpaBinary random errors approximated density
normalMoment

Calculate k-th order moment of normal distribution
dnorm_parallel

Calculate normal pdf in parallel
print.summary.hpaML

Summary for hpaML output
predict.hpaML

Predict method for hpaML
hpaSelection

Perform semi-nonparametric selection model estimation
print.summary.hpaSelection

Summary for "hpaSelection" object
plot_hpaSelection

Plot hpaSelection random errors approximated density
print.hpaBinary

Print method for "hpaBinary" object
plot.hpaML

Plot approximated marginal density using hpaML output
logLik.hpaBinary

Calculates log-likelihood for "hpaBinary" object
plot.hpaSelection

Plot hpaSelection random errors approximated density
predict_hpaSelection

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

Summarizing hpaML Fits
predict_hpaML

Predict method for hpaML
print.hpaML

Print method for "hpaML" object
mecdf

Calculates multivariate empirical cumulative distribution function
print.hpaSelection

Print method for "hpaSelection" object
predict_hpaBinary

Predict method for hpaBinary
predict.hpaSelection

Predict outcome and selection equation values from hpaSelection model
print_summary_hpaBinary

Summary for hpaBinary output
print_summary_hpaSelection

Summary for hpaSelection output
print.summary.hpaBinary

Summary for "hpaBinary" object
summary.hpaBinary

Summarizing hpaBinary Fits
print_summary_hpaML

Summary for hpaML output
summary_hpaML

Summarizing hpaML Fits
summary_hpaBinary

Summarizing hpaBinary Fits
summary_hpaSelection

Summarizing hpaSelection Fits
truncatedNormalMoment

Calculate k-th order moment of truncated normal distribution
summary.hpaSelection

Summarizing hpaSelection Fits