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 |