# Distributions Hermite Polynomial Approximation

## Documentation for package ‘hpa’ version 1.3.4

- DESCRIPTION file

## Help Pages

bspline | B-splines generation, estimation and combination
bsplineComb | B-splines generation, estimation and combination
bsplineEstimate | B-splines generation, estimation and combination
bsplineGenerate | B-splines generation, estimation and combination
coef.hpaBinary | Extract coefficients from a hpaBinary object
coef.hpaML | Extract coefficients from a hpaML object
coef.hpaSelection | Extract coefficients from a hpaSelection object
dhpa | Probabilities and Moments Hermite Polynomial Approximation
dhpa0 | Fast pdf and cdf for standardized univariate PGN distribution
dhpaDiff | Probabilities and Moments Hermite Polynomial Approximation
dhsa | Probabilities and Moments Hermite Spline Approximation
dnorm_parallel | Calculate normal pdf in parallel
dtrhpa | Probabilities and Moments Hermite Polynomial Approximation
ehpa | Probabilities and Moments Hermite Polynomial Approximation
ehpaDiff | Probabilities and Moments Hermite Polynomial Approximation
ehsa | Probabilities and Moments Hermite Spline Approximation
etrhpa | Probabilities and Moments Hermite Polynomial Approximation
hpaBinary | Semi-nonparametric single index binary choice model estimation
hpaDist | Probabilities and Moments Hermite Polynomial Approximation
hpaDist0 | Fast pdf and cdf for standardized univariate PGN distribution
hpaML | Semi-nonparametric maximum likelihood estimation
hpaSelection | Perform semi-nonparametric selection model estimation
hsaDist | Probabilities and Moments Hermite Spline Approximation
ihpa | Probabilities and Moments Hermite Polynomial Approximation
ihpaDiff | Probabilities and Moments Hermite Polynomial Approximation
itrhpa | Probabilities and Moments Hermite Polynomial Approximation
logLik.hpaBinary | Calculates log-likelihood for "hpaBinary" object
logLik.hpaML | Calculates log-likelihood for "hpaML" object
logLik.hpaSelection | Calculates log-likelihood for "hpaSelection" object
logLik_hpaBinary | Calculates log-likelihood for "hpaBinary" object
logLik_hpaML | Calculates log-likelihood for "hpaML" object
logLik_hpaSelection | Calculates log-likelihood for "hpaSelection" object
mecdf | Calculates multivariate empirical cumulative distribution function
normalMoment | Calculate k-th order moment of normal distribution
phpa | Probabilities and Moments Hermite Polynomial Approximation
phpa0 | Fast pdf and cdf for standardized univariate PGN distribution
plot.hpaBinary | Plot the approximated density of hpaBinary random errors
plot.hpaML | Plot approximated marginal density using hpaML output
plot.hpaSelection | Plot hpaSelection random errors approximated density
pnorm_parallel | Calculate normal cdf in parallel
polynomialIndex | Multivariate Polynomial Representation
predict.hpaBinary | Predict method for hpaBinary
predict.hpaML | Predict method for hpaML
predict.hpaSelection | Predict outcome and selection equation values from hpaSelection model
predict_hpaBinary | Predict method for hpaBinary
predict_hpaML | Predict method for hpaML
predict_hpaSelection | Predict outcome and selection equation values from hpaSelection model
print.hpaBinary | Print method for "hpaBinary" object
print.hpaML | Print method for "hpaML" object
print.hpaSelection | Print method for "hpaSelection" object
print.summary.hpaBinary | Summary for "hpaBinary" object
print.summary.hpaML | Summary for hpaML output
print.summary.hpaSelection | Summary for "hpaSelection" object
printPolynomial | Multivariate Polynomial Representation
print_summary_hpaBinary | Summary for hpaBinary output
print_summary_hpaML | Summary for hpaML output
print_summary_hpaSelection | Summary for hpaSelection output
qhpa | Probabilities and Moments Hermite Polynomial Approximation
rhpa | Probabilities and Moments Hermite Polynomial Approximation
summary.hpaBinary | Summarizing hpaBinary Fits
summary.hpaML | Summarizing hpaML Fits
summary.hpaSelection | Summarizing hpaSelection Fits
summary_hpaBinary | Summarizing hpaBinary Fits
summary_hpaML | Summarizing hpaML Fits
summary_hpaSelection | Summarizing hpaSelection Fits
truncatedNormalMoment | Calculate k-th order moment of truncated normal distribution
vcov.hpaBinary | Extract covariance matrix from a hpaBinary object
vcov.hpaML | Extract covariance matrix from a hpaML object
vcov.hpaSelection | Extract covariance matrix from a hpaSelection object