Algebraic Maximum Likelihood Estimators

---

Documentation for package ‘algebraic.mle’ version 0.9.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

algebraic.mle-package	'algebraic.mle': A package for algebraically operating on and generating maximum likelihood estimators from existing maximum likelihood estimators.
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'.