Package: FARS
Type: Package
Title: Factor-Augmented Regression Scenarios
Version: 0.8.0
Authors@R: c(person("Gian Pietro", "Bellocca", email = "gbellocc@est-econ.uc3m.es", role = c("aut", "cre")),
             person("Ignacio", "Garrón", role = c("aut")),
             person("Vladimir", "Rodríguez-Caballero", role = c("aut")),
             person("Esther", "Ruiz", role = c("aut")))
Maintainer: Gian Pietro Bellocca <gbellocc@est-econ.uc3m.es>
Description: Provides a comprehensive framework in R for modeling and
        forecasting economic scenarios based on multi-level dynamic
        factor model. The package enables users to: (i) extract global
        and group-specific factors using a flexible multi-level factor
        structure; (ii) compute asymptotically valid confidence regions
        for the estimated factors, accounting for uncertainty in the
        factor loadings; (iii) obtain estimates of the parameters of
        the factor-augmented quantile regressions together with their
        standard deviations; (iv) recover full predictive conditional
        densities from estimated quantiles; (v) obtain risk measures
        based on extreme quantiles of the conditional densities; (vi)
        estimate the conditional density and the corresponding extreme
        quantiles when the factors are stressed.
Depends: R (>= 3.5.0)
Imports: rlang, magrittr, ggplot2, plotly, sn, nloptr, ellipse,
        SyScSelection, quantreg, tidyr, dplyr, forcats, MASS, reshape2,
        stringr, stats,
Suggests: R.rsp, devtools, knitr, rmarkdown, markdown, openxlsx,
        readxl, zoo
VignetteBuilder: R.rsp
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 7.3.2
URL: https://arxiv.org/abs/2507.10679
NeedsCompilation: no
Packaged: 2026-06-17 08:22:19 UTC; root
Author: Gian Pietro Bellocca [aut, cre], Ignacio Garrón [aut], Vladimir
        Rodríguez-Caballero [aut], Esther Ruiz [aut]
Repository: https://cran.r-universe.dev
Date/Publication: 2026-02-17 11:00:02 UTC
RemoteUrl: https://github.com/cran/FARS
RemoteRef: HEAD
RemoteSha: 876d80b08e7b289edc7acd54a14c1f93c97dd35b