aic                     Generic method for obtaining the AIC of a
                        fitted distribution object fit.
aic.mle                 Method for obtaining the AIC of an 'mle'
                        object.
algebraic.mle           'algebraic.mle': A package for algebraically
                        operating on and generating maximum likelihood
                        estimators from existing maximum likelihood
                        estimators.
bias                    Generic method for computing the bias of an
                        estimator object.
bias.mle                Computes the bias of an 'mle' object assuming
                        the large sample approximation is valid and the
                        MLE regularity conditions are satisfied. In
                        this case, the bias is zero (or zero vector).
bias.mle_boot           Computes the estimate of the bias of a
                        'mle_boot' object.
confint.mle             Function to compute the confidence intervals of
                        'mle' objects.
confint.mle_boot        Method for obtained the confidence interval of
                        an 'mle_boot' object. Note: This impelements
                        the 'vcov' method defined in 'stats'.
confint_from_sigma      Function to compute the confidence intervals
                        from a variance-covariance matrix
expectation.mle         Expectation operator applied to 'x' of type
                        'mle' with respect to a function 'g'. That is,
                        'E(g(x))'.
is_mle                  Determine if an object 'x' is an 'mle' object.
is_mle_boot             Determine if an object is an 'mle_boot' object.
loglik_val              Generic method for obtaining the log-likelihood
                        value of a fitted MLE object.
loglik_val.mle          Method for obtaining the log-likelihood of an
                        'mle' object.
marginal.mle            Method for obtaining the marginal distribution
                        of an MLE that is based on asymptotic
                        assumptions:
mle                     Constructor for making 'mle' objects, which
                        provides a common interface for maximum
                        likelihood estimators.
mle_boot                Bootstrapped MLE
mle_numerical           This function takes the output of 'optim',
                        'newton_raphson', or 'sim_anneal' and turns it
                        into an 'mle_numerical' (subclass of 'mle')
                        object.
mle_weighted            Accepts a list of 'mle' objects for some
                        parameter, say 'theta', and combines them into
                        a single estimator 'mle_weighted'.
mse                     Generic method for computing the mean squared
                        error (MSE) of an estimator, 'mse(x) =
                        E[(x-mu)^2]' where 'mu' is the true parameter
                        value.
mse.mle                 Computes the MSE of an 'mle' object.
mse.mle_boot            Computes the estimate of the MSE of a 'boot'
                        object.
nobs.mle                Method for obtaining the number of observations
                        in the sample used by an 'mle'.
nobs.mle_boot           Method for obtaining the number of observations
                        in the sample used by an 'mle'.
nparams.mle             Method for obtaining the number of parameters
                        of an 'mle' object.
nparams.mle_boot        Method for obtaining the number of parameters
                        of an 'boot' object.
obs.mle                 Method for obtaining the observations used by
                        the 'mle' object 'x'.
obs.mle_boot            Method for obtaining the observations used by
                        the 'mle'.
observed_fim            Generic method for computing the observed FIM
                        of an 'mle' object.
observed_fim.mle        Function for obtaining the observed FIM of an
                        'mle' object.
orthogonal              Generic method for determining the orthogonal
                        parameters of an estimator.
orthogonal.mle          Method for determining the orthogonal
                        components of an 'mle' object 'x'.
params.mle              Method for obtaining the parameters of an 'mle'
                        object.
params.mle_boot         Method for obtaining the parameters of an
                        'boot' object.
pred                    Generic method for computing the predictive
                        confidence interval given an estimator object
                        'x'.
pred.mle                Estimate of predictive interval of 'T|data'
                        using Monte Carlo integration.
print.mle               Print method for 'mle' objects.
print.summary_mle       Function for printing a 'summary' object for an
                        'mle' object.
rmap.mle                Computes the distribution of 'g(x)' where 'x'
                        is an 'mle' object.
sampler.mle             Method for sampling from an 'mle' object.
sampler.mle_boot        Method for sampling from an 'mle_boot' object.
score_val               Generic method for computing the score of an
                        estimator object (gradient of its
                        log-likelihood function evaluated at the MLE).
score_val.mle           Computes the score of an 'mle' object (score
                        evaluated at the MLE).
se                      Generic method for obtaining the standard
                        errors of an estimator.
se.mle                  Function for obtaining an estimate of the
                        standard error of the MLE object 'x'.
summary.mle             Function for obtaining a summary of 'object',
                        which is a fitted 'mle' object.
vcov.mle                Computes the variance-covariance matrix of
                        'mle' object.
vcov.mle_boot           Computes the variance-covariance matrix of
                        'boot' object. Note: This impelements the
                        'vcov' method defined in 'stats'.
