Some EM-Type Estimation Methods for the Heckman Selection Model
Overview
Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM’s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.
- There is a modification with handling the missing data. We changed the warning message and new approach of handling missing data is adapted to both selection data and response data.
From NEWS.md in the release
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NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Sang Kyu Lee <leesa111@msu.edu>’
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Authors@R: c(person(given = "Kexuan",
family = "Yang",
role = "aut",
email = "717260446@qq.com"),
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role = c("aut", "cre"),
email = "leesa111@msu.edu"),
person(given = "Jun",
family = "Zhao",
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email = "zhaojun2021@hotmail.com"),
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role = "aut",
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as necessary.- NOTE11 OK, 2 NOTE2026-06-09
- ERROR11 OK, 1 NOTE, 1 ERROR2026-06-08
- NOTE12 OK, 2 NOTE2026-03-10
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Maintainer since 1.0.0 (2020)
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- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2020-05-29
- Total releases
- 5 / 6 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 5.1 KB / 1 file
- Download size
- 20 KB
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