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hpa (version 1.3.3)

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

1.3.3

License

GPL-3

Maintainer

Potanin Bogdan

Last Published

November 29th, 2023

Functions in hpa (1.3.3)

coef.hpaML

Extract coefficients from hpaML object
coef.hpaSelection

Extract coefficients from hpaSelection object
bspline

B-splines generation, estimation and combination
coef.hpaBinary

Extract coefficients from hpaBinary object
logLik_hpaSelection

Calculates log-likelihood for "hpaSelection" object
summary_hpaBinary

Summarizing hpaBinary Fits
summary_hpaML

Summarizing hpaML Fits
mecdf

Calculates multivariate empirical cumulative distribution function
predict.hpaBinary

Predict method for hpaBinary
predict.hpaML

Predict method for hpaML
print.summary.hpaML

Summary for hpaML output
print.summary.hpaSelection

Summary for "hpaSelection" object
vcov.hpaBinary

Extract covariance matrix from hpaBinary object
vcov.hpaML

Extract covariance matrix from hpaML object
logLik_hpaBinary

Calculates log-likelihood for "hpaBinary" object
hpaDist

Probabilities and Moments Hermite Polynomial Approximation
hpaDist0

Fast pdf and cdf for standardized univariate PGN distribution
normalMoment

Calculate k-th order moment of normal distribution
plot.hpaBinary

Plot hpaBinary random errors approximated density
hpaML

Semi-nonparametric maximum likelihood estimation
hpaSelection

Perform semi-nonparametric selection model estimation
logLik_hpaML

Calculates log-likelihood for "hpaML" object
hsaDist

Probabilities and Moments Hermite Spline Approximation
pnorm_parallel

Calculate normal cdf in parallel
logLik.hpaBinary

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

Predict outcome and selection equation values from hpaSelection model
dnorm_parallel

Calculate normal pdf in parallel
predict_hpaBinary

Predict method for hpaBinary
polynomialIndex

Multivariate Polynomial Representation
print.hpaBinary

Print method for "hpaBinary" object
predict_hpaML

Predict method for hpaML
predict_hpaSelection

Predict outcome and selection equation values from hpaSelection model
hpaBinary

Semi-nonparametric single index binary choice model estimation
print_summary_hpaSelection

Summary for hpaSelection output
print.hpaSelection

Print method for "hpaSelection" object
logLik.hpaML

Calculates log-likelihood for "hpaML" object
print.summary.hpaBinary

Summary for "hpaBinary" object
summary.hpaML

Summarizing hpaML Fits
summary.hpaSelection

Summarizing hpaSelection Fits
summary.hpaBinary

Summarizing hpaBinary Fits
logLik.hpaSelection

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

Print method for "hpaML" object
summary_hpaSelection

Summarizing hpaSelection Fits
plot.hpaML

Plot approximated marginal density using hpaML output
truncatedNormalMoment

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

Plot hpaSelection random errors approximated density
print_summary_hpaBinary

Summary for hpaBinary output
print_summary_hpaML

Summary for hpaML output
vcov.hpaSelection

Extract covariance matrix from hpaSelection object