Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>.
             Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. 
             Extensions to two-sample simultaneous mean comparison problems are also included. 
             As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.

Handy functions and data to support the course book 'Empirical Research in Accounting: Tools and Methods' (1st ed.). Chapman and Hall/CRC. <doi:10.1201/9781003456230> and <https://iangow.github.io/far_book/>.

Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, 
    select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. 
    When the model is executed a set of results are displayed which include efficiency scores, peers' determination, scale efficiencies' evaluation 
    and slacks' calculation. Fore more information about the theoretical background of the package, 
    please refer to Bogetoft & Otto (2011) <doi:10.1007/978-1-4419-7961-2>.

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.