# Distributions Hermite Polynomial Approximation

## Documentation for package ‘hpa’ version 1.1.1

- DESCRIPTION file.

## Help Pages

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
AIC_hpaML | Calculates AIC for "hpaML" object
AIC_hpaSelection | Calculates AIC for "hpaSelection" object
dhpa | Density function hermite polynomial approximation
dhpaDiff | Calculate gradient of density function hermite polynomial approximation
dnorm_parallel | Calculate normal pdf in parallel
dtrhpa | Truncated density function hermite polynomial approximation
ehpa | Expected powered product hermite polynomial approximation
etrhpa | Expected powered product hermite polynomial approximation for truncated distribution
hpaBinary | Perform semi-nonparametric binary choice model estimation
hpaML | Semi-nonparametric maximum likelihood estimation
hpaSelection | Perform semi-nonparametric selection model estimation
ihpa | Interval distribution function hermite polynomial approximation
ihpaDiff | Calculate gradient of interval distribution function hermite polynomial approximation
itrhpa | Truncated interval distribution function hermite polynomial approximation for truncated distribution
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 | Distribution function hermite polynomial approximation
plot.hpaBinary | Plot hpaBinary random errors approximated density
plot.hpaSelection | Plot hpaSelection random errors approximated density
plot_hpaBinary | Plot hpaBinary random errors approximated density
plot_hpaSelection | Plot hpaSelection random errors approximated density
pnorm_parallel | Calculate normal cdf in parallel
polynomialIndex | Returns matrix of polynomial indexes
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.summary.hpaBinary | Summary for hpaBinary output
print.summary.hpaML | Summary for hpaML output
print.summary.hpaSelection | Summary for hpaSelection output
printPolynomial | Print polynomial given it's degrees and coefficients
print_summary_hpaBinary | Summary for hpaBinary output
print_summary_hpaML | Summary for hpaML output
print_summary_hpaSelection | Summary for hpaSelection output
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