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mable (version 4.1.1)

Maximum Approximate Bernstein/Beta Likelihood Estimation

Description

Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) and Guan (2017) . For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.

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Install

install.packages('mable')

Monthly Downloads

242

Version

4.1.1

License

LGPL (>= 2.0, < 3)

Maintainer

Zhong Guan

Last Published

October 1st, 2024

Functions in mable (4.1.1)

Some Bivariate Copulas

MABLE in Desnity Ratio Model

Maximum Approximate Bernstein Likelihood Estimate of Multivariate Density Function

Minimum Approximate Distance Estimate of Density Function with an optimal model degree

Minimum Approximate Distance Estimate of univariate Density Function with given model degree(s)

Minimum Approximate Distance Estimate of Copula Density

Minimum Approximate Distance Estimate of Copula with given model degrees

Mable fit of proportional odds rate regression model

Mable fit of semiparametric regression model based on interval censored data

Mable fit of AFT model with given regression coefficients

Minimum Approximate Distance Estimate of Multivariate Density Function

Mable fit of Cox's proportional hazards regression model

Mable fit of the PH model with given regression coefficients

Maximum approximate profile likelihood estimate of the density ratio model

The mixing proportions of marginal distribution from the mixture of multivariate beta distribution

Component Beta cumulative distribution functions of the Bernstein polynomial model

mable with degree selected by the method of moment and method of mode

Multivariate empirical cumulative distribution evaluated at sample data

Choosing optimal model degree by gamma change-point method

Maximum approximate profile likelihood estimate of the density ratio model for grouped data with given regression coefficients

Summary mathods for classes 'mable' and 'mable_reg'

Generalized PO model with Weibull baseline

Method of mode estimate of a Bernstein polynomial model degree

Plot mathod for class 'mable'

Plot mathod for class 'mable_reg'

Matrix of the uniform marginal constraints

Mable fit of the PO model with given regression coefficients

Standard errors of coefficients in density ratio model

Pancreatic Cancer Biomarker Data

Vaal River Annual Flow Data

Exponential change-point

Multivariate Mixture Beta Distribution

Breast cosmesis data

Chicken Embryo Data

Some Parametric Conditional Bivariate Copulas

Exponentially Tilted Mixture Beta Distribution

Mable fit of the density ratio model based on grouped data

Mixture Beta Distribution

Maximum Approximate Bernstein Likelihood Estimate of Copula Density Function

Bhattacharyya coefficient and Hellinger correlation

Maximum Approximate Bernstein Likelihood Estimate of Univariate or Multivariate Density Function

Mable deconvolution with a known error density

Estimate of Hellinger Correlation between two random variables and Bootstrap

Mable fit based on one-sample interval censored data

Control parameters for mable fit

Mable fit of one-sample grouped data by an optimal or a preselected model degree

Mable fit of Accelerated Failure Time Model