Man pages for hpa
Distributions Hermite Polynomial Approximation
| bspline | 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 |
| dnorm_parallel | Calculate normal pdf in parallel |
| hpaBinary | Semi-nonparametric single index binary choice model... |
| 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 |
| logLik_hpaBinary | Calculates log-likelihood for "hpaBinary" object |
| logLik.hpaBinary | Calculates log-likelihood for "hpaBinary" object |
| logLik_hpaML | Calculates log-likelihood for "hpaML" object |
| logLik.hpaML | Calculates log-likelihood for "hpaML" object |
| logLik_hpaSelection | Calculates log-likelihood for "hpaSelection" object |
| logLik.hpaSelection | Calculates log-likelihood for "hpaSelection" object |
| mecdf | Calculates multivariate empirical cumulative distribution... |
| normalMoment | Calculate k-th order moment of normal 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.hpaBinary | Predict method for hpaBinary |
| predict_hpaML | Predict method for hpaML |
| predict.hpaML | Predict method for hpaML |
| predict_hpaSelection | Predict outcome and selection equation values from... |
| predict.hpaSelection | Predict outcome and selection equation values from... |
| 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 output |
| print.summary.hpaBinary | Summary for "hpaBinary" object |
| print_summary_hpaML | Summary for hpaML output |
| print.summary.hpaML | Summary for hpaML output |
| print_summary_hpaSelection | Summary for hpaSelection output |
| print.summary.hpaSelection | Summary for "hpaSelection" object |
| summary_hpaBinary | Summarizing hpaBinary Fits |
| summary.hpaBinary | Summarizing hpaBinary Fits |
| summary_hpaML | Summarizing hpaML Fits |
| summary.hpaML | Summarizing hpaML Fits |
| summary_hpaSelection | Summarizing hpaSelection 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 |
