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
dtrhpa                  Truncated density function hermite polynomial
                        approximation
ehpa                    Expected powered product hermite polynomial
                        approximation
etrhpa                  Expected powered product hermite polynomial
                        approximation for truncated distribution
hpa-package             Provides Tools for Multivariate Distributions
                        Hermite Polynomial Approximation
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
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
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
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
