Package: PAGFL Title: Joint Estimation of Latent Groups and Group-Specific Coefficients in (Time-Varying) Panel Data Models Version: 1.2.0 Authors@R: c( person("Paul", "Haimerl", email = "paul.haimerl@econ.au.dk", role = c("aut","cre"), comment = c(ORCID = "0000-0003-3198-8317")), person("Stephan", "Smeekes", email = "s.smeekes@maastrichtuniversity.nl", role = "ctb", comment = c(ORCID = "0000-0002-0157-639X")), person("Ines", "Wilms", email = "i.wilms@maastrichtuniversity.nl", role = "ctb", comment = c(ORCID = "0000-0003-3269-4601")), person("Ali", "Mehrabani", email = "ali.mehrabani@ku.edu", role = "ctb", comment = c(ORCID = "0000-0002-1848-5582"))) Maintainer: Paul Haimerl Description: Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) . PAGFL identifies latent group structures and group-specific coefficients in a single step. On top of that, we extend the PAGFL to time-varying coefficient functions (FUSE-TIME), following Haimerl et al. (2025) . Depends: R (>= 3.5.0) License: AGPL (>= 3) Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.2 LinkingTo: Rcpp, RcppArmadillo, RcppParallel, RcppThread Imports: Rcpp, lifecycle, ggplot2, RcppParallel, RhpcBLASctl BugReports: https://github.com/Paul-Haimerl/PAGFL/issues URL: https://github.com/Paul-Haimerl/PAGFL Suggests: testthat (>= 3.0.0) Config/testthat/edition: 3 Config/pak/sysreqs: cmake Repository: https://paul-haimerl.r-universe.dev Date/Publication: 2026-05-12 11:58:44 UTC RemoteUrl: https://github.com/paul-haimerl/pagfl RemoteRef: HEAD RemoteSha: b2fd55eb0e063d395ac99b7da5580e7c0c713563 NeedsCompilation: yes Packaged: 2026-10-09 08:14:17 UTC; root Author: Paul Haimerl [aut, cre] (ORCID: ), Stephan Smeekes [ctb] (ORCID: ), Ines Wilms [ctb] (ORCID: ), Ali Mehrabani [ctb] (ORCID: )