PLreg
0.4.1Power Logit Regression for Modeling Bounded Data
Overview
Power logit regression models for bounded continuous data, in which the density generator may be normal, Student-t, power exponential, slash, hyperbolic, sinh-normal, or type II logistic. Diagnostic tools associated with the fitted model, such as the residuals, local influence measures, leverage measures, and goodness-of-fit statistics, are implemented. The estimation process follows the maximum likelihood approach and, currently, the package supports two types of estimators: the usual maximum likelihood estimator and the penalized maximum likelihood estimator. More details about power logit regression models are described in Queiroz and Ferrari (2022) arXiv:2202.01697.
Install
Health
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- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 83%
- Return-value docs
- 100%
- References docs
- 71%
Downloads
Repository
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Repository practices
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Dependencies
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Code & Tests
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Package metadata
- First published
- 2022-03-30
- Total releases
- 5 / 4 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 6.8 KB / 3 files
- Download size
- 46 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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