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In panel data analysis, unobservable group structures are a common challenge. Disregarding group-level heterogeneity by assuming an entirely homogeneous panel can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses this issue by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) <doi:10.1016/j.jeconom.2022.12.002>. PAGFL is an efficient methodology to identify latent group structures and estimate group-specific coefficients simultaneously.
Version: | 1.0.1 |
Imports: | Rcpp, pbapply |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2024-02-17 |
Author: | Paul Haimerl [aut, cre], Ali Mehrabani [ctb] |
Maintainer: | Paul Haimerl <paul.haimerl at maastrichtuniversity.nl> |
BugReports: | https://github.com/Paul-Haimerl/PAGFL/issues |
License: | AGPL (≥ 3) |
URL: | https://github.com/Paul-Haimerl/PAGFL |
NeedsCompilation: | yes |
Materials: | README NEWS |
CRAN checks: | PAGFL results |
Reference manual: | PAGFL.pdf |
Package source: | PAGFL_1.0.1.tar.gz |
Windows binaries: | r-devel: PAGFL_1.0.1.zip, r-release: PAGFL_1.0.1.zip, r-oldrel: PAGFL_1.0.1.zip |
macOS binaries: | r-release (arm64): PAGFL_1.0.1.tgz, r-oldrel (arm64): PAGFL_1.0.1.tgz, r-release (x86_64): PAGFL_1.0.1.tgz, r-oldrel (x86_64): PAGFL_1.0.1.tgz |
Old sources: | PAGFL archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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