Learn R Programming

LogConcDEAD (version 1.6-10)

Log-Concave Density Estimation in Arbitrary Dimensions

Description

Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, ).

Copy Link

Version

Install

install.packages('LogConcDEAD')

Monthly Downloads

593

Version

1.6-10

License

GPL (>= 2)

Maintainer

Last Published

September 11th, 2024

Functions in LogConcDEAD (1.6-10)

Evaluation of a smoothed log-concave maximum likelihood estimator at given points

Construct an object of class LogConcDEAD

plot.LogConcDEAD

Plot a log-concave maximum likelihood estimator

print.LogConcDEAD

Summarizing log-concave maximum likelihood estimator

Evaluation of a log-concave maximum likelihood estimator at a point

Evaluate the marginal of multivariate log-concave maximum likelihood estimators at a point

A GUI for classification in two dimensions using smoothed log-concave

Find appropriate weights for likelihood calculations

Estimate the mixture proportions and component densities using EM algorithm

Finds marginals of multivariate logconcave maximum likelihood estimators by integrating

LogConcDEAD-package

Computes a log-concave (maximum likelihood) estimator for i.i.d. data in any number of dimensions

Evaluate the log-concave maximum likelihood estimator of 2-d data on a grid for plotting

LogConcDEAD-internal

Internal log-concave maximum likelihood estimation functions

cov.LogConcDEAD

Compute the covariance matrix of a log-concave maximum likelihood estimator

Compute the smoothing matrix of the smoothed log-concave maximum likelihood estimator

Sample from a log-concave maximum likelihood estimate

Sample from a smoothed log-concave maximum likelihood estimate

Compute the maximum likelihood estimator of a log-concave density