Algebraic Maximum Likelihood Estimators

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Documentation for package ‘algebraic.mle’ version 2.0.2

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as_dist.mle_fit Convert an MLE to a distribution object.
as_dist.mle_fit_boot Convert a bootstrap MLE to an empirical distribution.
bias Generic method for computing the bias of an estimator object.
bias.mle_fit Computes the bias of an 'mle_fit' 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_fit_boot Computes the estimate of the bias of a 'mle_fit_boot' object.
cdf.mle_fit CDF of the asymptotic distribution of an MLE.
coef.mle_fit Extract coefficients from an 'mle_fit' object.
combine Combine independent MLEs for the same parameter.
combine.list Combine independent MLEs for the same parameter.
combine.mle_fit Combine independent MLEs for the same parameter.
conditional.mle_fit Conditional distribution from an MLE.
confint.mle_fit Function to compute the confidence intervals of 'mle_fit' objects.
confint.mle_fit_boot Method for obtained the confidence interval of an 'mle_fit_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
density.mle_fit PDF of the asymptotic distribution of an MLE.
density.mle_fit_boot PDF of the empirical distribution of bootstrap replicates.
dim.mle_fit Dimension (number of parameters) of an MLE.
dim.mle_fit_boot Dimension (number of parameters) of a bootstrap MLE.
expectation.mle_fit Expectation operator applied to 'x' of type 'mle_fit' with respect to a function 'g'. That is, 'E(g(x))'.
inv_cdf.mle_fit Quantile function of the asymptotic distribution of an MLE.
is_mle Determine if an object 'x' is an 'mle_fit' object.
is_mle_boot Determine if an object is an 'mle_fit_boot' object.
joint Compose independent MLEs into a joint MLE.
joint.mle_fit Compose independent MLEs into a joint MLE.
logLik.mle_fit Log-likelihood of an 'mle_fit' object.
marginal.mle_fit Method for obtaining the marginal distribution of an MLE that is based on asymptotic assumptions:
mean.mle_fit Mean of the asymptotic distribution of an MLE.
mean.mle_fit_boot Mean of bootstrap replicates.
mle Constructor for making 'mle_fit' 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_fit_numerical' (subclass of 'mle_fit') object.
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_fit Computes the MSE of an 'mle_fit' object.
mse.mle_fit_boot Computes the estimate of the MSE of a 'boot' object.
nobs.mle_fit Method for obtaining the number of observations in the sample used by an 'mle_fit'.
nobs.mle_fit_boot Method for obtaining the number of observations in the sample used by an 'mle_fit_boot'.
nparams.mle_fit Method for obtaining the number of parameters of an 'mle_fit' object.
nparams.mle_fit_boot Method for obtaining the number of parameters of an 'boot' object.
obs.mle_fit Method for obtaining the observations used by the 'mle_fit' object 'x'.
obs.mle_fit_boot Method for obtaining the observations used by the 'mle_fit_boot'.
observed_fim Generic method for computing the observed FIM of an 'mle_fit' object.
observed_fim.mle_fit Function for obtaining the observed FIM of an 'mle_fit' object.
orthogonal Generic method for determining the orthogonal parameters of an estimator.
orthogonal.mle_fit Method for determining the orthogonal components of an 'mle_fit' object 'x'.
params.mle_fit Method for obtaining the parameters of an 'mle_fit' object.
params.mle_fit_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_fit Estimate of predictive interval of 'T|data' using Monte Carlo integration.
print.mle_fit Print method for 'mle_fit' objects.
print.summary_mle_fit Function for printing a 'summary' object for an 'mle_fit' object.
rmap.mle_fit Computes the distribution of 'g(x)' where 'x' is an 'mle_fit' object.
sampler.mle_fit Method for sampling from an 'mle_fit' object.
sampler.mle_fit_boot Method for sampling from an 'mle_fit_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_fit Computes the score of an 'mle_fit' object (score evaluated at the MLE).
se Generic method for obtaining the standard errors of an estimator.
se.mle_fit Function for obtaining an estimate of the standard error of the MLE object 'x'.
summary.mle_fit Function for obtaining a summary of 'object', which is a fitted 'mle_fit' object.
sup.mle_fit Support of the asymptotic distribution of an MLE.
vcov.mle_fit Computes the variance-covariance matrix of 'mle_fit' object.
vcov.mle_fit_boot Computes the variance-covariance matrix of 'boot' object. Note: This impelements the 'vcov' method defined in 'stats'.