bspline | B-splines generation, estimation and combination |
coef.hpaBinary | Extract coefficients from hpaBinary object |
coef.hpaML | Extract coefficients from hpaML object |
coef.hpaSelection | Extract coefficients from 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 hpaBinary random errors approximated density |
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 hpaBinary object |
vcov.hpaML | Extract covariance matrix from hpaML object |
vcov.hpaSelection | Extract covariance matrix from hpaSelection object |
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