HeckmanStan: Heckman Selection Models Based on Bayesian Analysis

Implements Heckman selection models using a Bayesian approach via 'Stan' and compares the performance of normal, Student’s t, and contaminated normal distributions in addressing complexities and selection bias (Heeju Lim, Victor E. Lachos, and Victor H. Lachos, Bayesian analysis of flexible Heckman selection models using Hamiltonian Monte Carlo, 2025, under submission).

Version:	1.0.0
Depends:	R (≥ 3.5.0)
Imports:	rstan (≥ 2.26.23), mvtnorm (≥ 1.2-3), loo, stats
Published:	2025-05-06
Author:	Heeju Lim [aut, cre], Victor E. Lachos [aut], Victor H. Lachos [aut]
Maintainer:	Heeju Lim <heeju.lim at uconn.edu>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	HeckmanStan results

Documentation:

Reference manual:

HeckmanStan.html , HeckmanStan.pdf

Downloads:

Package source:	HeckmanStan_1.0.0.tar.gz
Windows binaries:	r-devel: HeckmanStan_1.0.0.zip, r-release: HeckmanStan_1.0.0.zip, r-oldrel: HeckmanStan_1.0.0.zip
macOS binaries:	r-release (arm64): HeckmanStan_1.0.0.tgz, r-oldrel (arm64): HeckmanStan_1.0.0.tgz, r-release (x86_64): HeckmanStan_1.0.0.tgz, r-oldrel (x86_64): HeckmanStan_1.0.0.tgz

Linking:

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