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double.truncation (version 1.8)

Analysis of Doubly-Truncated Data

Description

Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) and Emura, Konno, and Michimae (2015) . Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) and Emura, Hu and Konno (2017) . The parametric location-scale models are based on Dorre et al. (2021) .

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Version

Install

install.packages('double.truncation')

Monthly Downloads

215

Version

1.8

License

GPL-2

Maintainer

Takeshi Emura

Last Published

December 5th, 2024

Functions in double.truncation (1.8)

PMLE.SEF1.negative

Parametric inference for the one-parameter SEF model (negative parameter space)

Nonparametric inference based on the self-consistency method

double.truncation-package

Analysis of Doubly-Truncated Data

PMLE.SEF3.positive

Parametric Inference for the three-parameter SEF model (positive parameter space for eta_3)

PMLE.SEF3.negative

Parametric Inference for the three-parameter SEF model (negative parameter space for eta_3)

PMLE.loglogistic

Parametric Inference for the log-logistic model

Parametric inference for the one-parameter SEF model (free parameter space)

Goodness-of-fit test based on the CvM and KS statistics

Parametric Inference for the Weibull model

PMLE.SEF1.positive

Parametric Inference for the one-parameter SEF model (positive parameter space)

Parametric Inference for the lognormal model

Parametric Inference for the three-parameter SEF model (free parameter space for eta_3)

PMLE.SEF2.negative

Parametric Inference for the two-parameter SEF model (negative parameter space for eta_2)

Simulating doubly-truncated data from the Weibull model