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  "Title": "Joint Estimation of Latent Groups and Group-Specific\nCoefficients in (Time-Varying) Panel Data Models",
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  "Authors@R": "c(\nperson(\"Paul\", \"Haimerl\", email = \"paul.haimerl@econ.au.dk\", role = c(\"aut\",\"cre\"), comment = c(ORCID = \"0000-0003-3198-8317\")),\nperson(\"Stephan\", \"Smeekes\", email = \"s.smeekes@maastrichtuniversity.nl\", role = \"ctb\", comment = c(ORCID = \"0000-0002-0157-639X\")),\nperson(\"Ines\", \"Wilms\", email = \"i.wilms@maastrichtuniversity.nl\", role = \"ctb\", comment = c(ORCID = \"0000-0003-3269-4601\")),\nperson(\"Ali\", \"Mehrabani\", email = \"ali.mehrabani@ku.edu\", role = \"ctb\", comment = c(ORCID = \"0000-0002-1848-5582\")))",
  "Maintainer": "Paul Haimerl <paul.haimerl@econ.au.dk>",
  "Description": "Latent group structures are a common challenge in panel\ndata analysis. Disregarding group-level heterogeneity can\nintroduce bias. Conversely, estimating individual coefficients\nfor each cross-sectional unit is inefficient and may lead to\nhigh uncertainty. This package addresses the issue of\nunobservable group structures by implementing the pairwise\nadaptive group fused Lasso (PAGFL) by Mehrabani (2023)\n<doi:10.1016/j.jeconom.2022.12.002>. PAGFL identifies latent\ngroup structures and group-specific coefficients in a single\nstep. On top of that, we extend the PAGFL to time-varying\ncoefficient functions (FUSE-TIME), following Haimerl et al.\n(2025) <doi:10.48550/arXiv.2503.23165>.",
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