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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-package 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> )
Method for obtaining the number of observations in the sample used by
an `mle` object `x`.
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`.