Package: PAGFL 1.1.2

Paul Haimerl

PAGFL: Joint Estimation of Latent Groups and Group-Specific Coefficients in Panel Data Models

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) <doi:10.1016/j.jeconom.2022.12.002>. 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.

Authors:Paul Haimerl [aut, cre], Stephan Smeekes [ctb], Ines Wilms [ctb], Ali Mehrabani [ctb]

PAGFL_1.1.2.tar.gz
PAGFL_1.1.2.tar.gz(r-4.5-noble)PAGFL_1.1.2.tar.gz(r-4.4-noble)
PAGFL_1.1.2.tgz(r-4.4-emscripten)PAGFL_1.1.2.tgz(r-4.3-emscripten)
PAGFL.pdf |PAGFL.html
PAGFL/json (API)
NEWS

# Install 'PAGFL' in R:
install.packages('PAGFL', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/paul-haimerl/pagfl/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

2.30 score 3 scripts 172 downloads 7 exports 31 dependencies

Last updated 26 days agofrom:90e53174db. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 11 2024
R-4.5-linux-x86_64OKNov 11 2024

Exports:grouped_plmgrouped_tv_plmpagflPAGFLsim_DGPsim_tv_DGPtv_pagfl

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppParallelrlangscalestibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Grouped Panel Data Modelcoef.gplm df.residual.gplm fitted.gplm formula.gplm grouped_plm print.gplm residuals.gplm summary.gplm
Grouped Time-varying Panel Data Modelcoef.tv_gplm df.residual.tv_gplm fitted.tv_gplm formula.tv_gplm grouped_tv_plm print.tv_gplm residuals.tv_gplm summary.tv_gplm
Pairwise Adaptive Group Fused Lassocoef.pagfl df.residual.pagfl fitted.pagfl formula.pagfl PAGFL pagfl print.pagfl residuals.pagfl summary.pagfl
Simulate a Panel With a Group Structure in the Slope Coefficientssim_DGP
Simulate a Time-varying Panel With a Group Structure in the Slope Coefficientssim_tv_DGP
Time-varying Pairwise Adaptive Group Fused Lassocoef.tvpagfl df.residual.tvpagfl fitted.tvpagfl formula.tvpagfl print.tvpagfl residuals.tvpagfl summary.tvpagfl tv_pagfl