| %||% | Null default operator |
| AIC.mlmodel | Extract AIC from mlmodel objects |
| AIC.summary.mlmodel | Extract AIC from mlmodel objects |
| BIC.mlmodel | Extract BIC from mlmodel objects |
| BIC.summary.mlmodel | Extract BIC from mlmodel objects |
| coef.mlmodel | Extract Model Coefficients |
| confint.mlmodel | Confidence Intervals for mlmodel Coefficients |
| docvis | U.S. Medical Expenditure Panel Survey |
| find_predictors.mlmodel | Extract the predictors used in the model (for insight/marginaleffects compatibility) |
| find_variables.mlmodel | Extract the variables used in the model (for insight/marginaleffects compatibility) |
| fitted.mlmodel | Extract Fitted Values from mlmodel |
| fitted.values.mlmodel | Extract Fitted Values from mlmodel |
| formula.mlmodel | Extract value formula from mlmodel objects |
| get_data.mlmodel | Extract data used to fit the model (for insight/marginaleffects compatibility) |
| get_modeldata.mlmodel | Extract data used to fit the model (for insight/marginaleffects compatibility) |
| GOFtest | Goodness-of-Fit Test for Count Models |
| GOFtest.mlmodel | Goodness-of-Fit Test for Count Models |
| gradientObs | Gradient (Score) by Observation |
| gradientObs.mlmodel | Gradient (Score) by Observation |
| hessianObs | Hessian by Observation |
| hessianObs.mlmodel | Hessian by Observation |
| IMtest | Information Matrix Test for Model Misspecification |
| IMtest.mlmodel | Information Matrix Test for Model Misspecification |
| logLik.mlmodel | Extract Log-Likelihood from mlmodel objects |
| logLik.summary.mlmodel | Extract Log-Likelihood from mlmodel objects |
| loglikeObs | Log-Likelihood by Observation |
| loglikeObs.mlmodel | Log-Likelihood by Observation |
| lrtest | Likelihood Ratio Test for Nested mlmodel Objects |
| lrtest.mlmodel | Likelihood Ratio Test for Nested mlmodel Objects |
| ml_beta | Fit Beta Model by Maximum Likelihood |
| ml_gamma | Fit Gamma Model by Maximum Likelihood |
| ml_lm | Fit linear model by Maximum Likelihood |
| ml_logit | Fit Binary Logit Model by Maximum Likelihood |
| ml_negbin | Fit negative binomial models by Maximum Likelihood |
| ml_poisson | Fit Poisson model by Maximum Likelihood |
| ml_probit | Fit Binary Probit Model by Maximum Likelihood |
| mroz | University of Michigan Panel Study of Income Dynamics (PSID) |
| nobs.mlmodel | Extract the Number of Observations from an mlmodel |
| null-default | Null default operator |
| OVDtest | Overdispersion Tests for Count Models |
| predict.mlmodel | Predictions for mlmodel models |
| predict.ml_beta | Predictions for mlmodel models |
| predict.ml_gamma | Predictions for mlmodel models |
| predict.ml_lm | Predictions for mlmodel models |
| predict.ml_logit | Predictions for mlmodel models |
| predict.ml_negbin | Predictions for mlmodel models |
| predict.ml_poisson | Predictions for mlmodel models |
| predict.ml_probit | Predictions for mlmodel models |
| pw401k | 401(k) Participation Rates |
| residuals.mlmodel | Extract Model Residuals |
| se | Extract Standard Errors from mlmodel Objects |
| se.mlmodel | Extract Standard Errors from mlmodel Objects |
| smoke | 1979 National Health Interview Survey |
| summary.mlmodel | Summary for mlmodel objects |
| summary.ml_beta | Summary for mlmodel objects |
| summary.ml_gamma | Summary for mlmodel objects |
| summary.ml_lm | Summary for mlmodel objects |
| summary.ml_logit | Summary for mlmodel objects |
| summary.ml_negbin | Summary for mlmodel objects |
| summary.ml_poisson | Summary for mlmodel objects |
| summary.ml_probit | Summary for mlmodel objects |
| update.mlmodel | Update an mlmodel Call |
| update.ml_poisson | Update an mlmodel Call |
| vcov.mlmodel | Variance-Covariance Matrix for mlmodel Objects |
| vuongtest | Vuong's Test for Non-Nested Models |
| vuongtest.mlmodel | Vuong's Test for Non-Nested Models |
| waldtest | Wald Test for Linear Restrictions |
| waldtest.mlmodel | Wald Test for Linear Restrictions |