R: Maximum Likelihood Models and Tools for Estimation, Prediction, and Testing

# Maximum Likelihood Models and Tools for Estimation, Prediction, and Testing

## Documentation for package ‘mlmodels’ version 0.1.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

%||% | 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