Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1),
  e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>.
  Moreover, extended-support beta regression models can accommodate dependent variables with
  boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) <doi:10.1093/jrsssc/qlaf039>.
  For the classical beta regression model, alternative specifications are provided:
  Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for
  beta regression, see Grün, Kosmidis, and Zeileis (2012) <doi:10.18637/jss.v048.i11>.

Generates robust confidence intervals for standardized regression coefficients
    using heteroskedasticity-consistent standard errors for models fitted by lm()
    as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>.
    The package can also be used to generate confidence intervals for R-squared,
    adjusted R-squared, and differences of standardized regression coefficients.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

It computes betas-select, coefficients after standardization in
  structural equation models and regression models, standardizing only selected
  variables. Supports models with moderation, with product terms formed after
  standardization. It also offers confidence intervals that account for
  standardization, including bootstrap confidence intervals as proposed by
  Cheung et al. (2022) <doi:10.1037/hea0001188>.

Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).

The sample size according to the Bethel's procedure.

It provides access to and information about the most important
    Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>,
    the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography
    and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g.
    generating dynamic reports with a complete analysis of a series) and exporting
    these time series.