EMSS: Some EM-Type Estimation Methods for the Heckman Selection Model

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>.

Version:	1.1.1
Depends:	R (≥ 2.10)
Imports:	sampleSelection, mvtnorm
Suggests:	testthat
Published:	2022-01-10
DOI:	10.32614/CRAN.package.EMSS
Author:	Kexuan Yang, Sang Kyu Lee, Jun Zhao, and Hyoung-Moon Kim
Maintainer:	Sang Kyu Lee <leesa111 at msu.edu>
BugReports:	https://github.com/SangkyuStat/EMSS/issues
License:	GPL-2
URL:	https://github.com/SangkyuStat/EMSS
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	EMSS results

Documentation:

Reference manual:

EMSS.pdf

Downloads:

Package source:	EMSS_1.1.1.tar.gz
Windows binaries:	r-devel: EMSS_1.1.1.zip, r-release: EMSS_1.1.1.zip, r-oldrel: EMSS_1.1.1.zip
macOS binaries:	r-release (arm64): EMSS_1.1.1.tgz, r-oldrel (arm64): EMSS_1.1.1.tgz, r-release (x86_64): EMSS_1.1.1.tgz, r-oldrel (x86_64): EMSS_1.1.1.tgz
Old sources:	EMSS archive

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