| 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 |