AIC.gmnl
(Package: gmnl) :
Akaike's Information Criterion
Calculate the Akaike's information Criterion (AIC) or the Bayesian information Criterion (BIC) for an object of class gmnl.
● Data Source:
CranContrib
● Keywords:
● Alias: AIC.gmnl, BIC.gmnl
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bread.gmnl
(Package: gmnl) :
Bread for Sandwiches
Computes the “bread” of the sandwich covariance matrix for objects of class gmnl.
● Data Source:
CranContrib
● Keywords:
● Alias: bread.gmnl
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wtp.gmnl
(Package: gmnl) :
Compute Willingness-to-pay
Compute the willingness-to-pay.
● Data Source:
CranContrib
● Keywords:
● Alias: wtp.gmnl
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cov.gmnl
(Package: gmnl) :
Functions for Correlated Random Parameters
These are a set of functions that help to extract the variance-covariance matrix, the correlation matrix, and the standard error of the random parameters for models of class gmnl.
● Data Source:
CranContrib
● Keywords:
● Alias: cor.gmnl, cov.gmnl, se.cov.gmnl
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gFormula
(Package: gmnl) :
Model Formula for Multinomial Logit Models
Four kind of variables are used in multinomial choice models with individual heterogeneity: alternative specific and individual specific variables; variables for the mean of the random parameters (deterministic taste variations), and variables for the scale function. gFormula deals with this type of models using suitable methods to extract the elements of the model.
● Data Source:
CranContrib
● Keywords:
● Alias: gFormula, is.gFormula, model.frame.gFormula, model.matrix.gFormula
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plot.gmnl
(Package: gmnl) :
Plot of the Distribution of the Conditional Expectation of Random Parameters
Methods for gmnl objects which provide a plot of the distribution of the conditional expectation of the random parameters or the distribution of the conditional willigness-to-pay.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.gmnl
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getSummary.gmnl
(Package: gmnl) :
Get Model Summaries for Use with "mtable"
A generic function to collect coefficients and summary statistics from a gmnl object. It is used in mtable.
● Data Source:
CranContrib
● Keywords:
● Alias: getSummary.gmnl
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gmnl
(Package: gmnl) :
Estimate Multinomial Logit Models with Observed and Unobserved Individual Heterogeneity.
Estimate different types of multinomial logit models with observed and unobserved individual heterogneity, such as MIXL, S-MNL, G-MNL, LC and MM-MNL models. These models are estimated using Maximum Simulated Likelihood. It supports both cross-sectional and panel data.
● Data Source:
CranContrib
● Keywords:
● Alias: coef.gmnl, df.residual.gmnl, fitted.gmnl, gmnl, logLik.gmnl, model.matrix.gmnl, nObs.gmnl, print.gmnl, print.summary.gmnl, residuals.gmnl, summary.gmnl, update.gmnl
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effect.gmnl
(Package: gmnl) :
Get the Conditional Individual Coefficients
This a helper function to obtain the individuals' conditional estimate of the either random parameters or willingness-to-pay.
● Data Source:
CranContrib
● Keywords:
● Alias: effect.gmnl
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estfun.gmnl
(Package: gmnl) :
Gradient for Observations
It extracts the gradient for each observation evaluated at the estimated parameters for an object of class gmnl.
● Data Source:
CranContrib
● Keywords:
● Alias: estfun.gmnl
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providing 
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