Functions for evaluating and testing asset pricing models, including
    estimation and testing of factor risk premia, selection of "strong" risk 
    factors (factors having nonzero population correlation with test asset
    returns), heteroskedasticity and autocorrelation robust covariance matrix
    estimation and testing for model misspecification and identification. 
    The functions for estimating and testing factor risk 
    premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass 
    approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) 
    <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk
    premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The 
    functions for selecting the "strong" risk factors are based on the Oracle
    estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the 
    factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. 
    The functions for evaluating model misspecification implement the HJ
    model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>,
    which is a modification of the prominent Hansen-Jagannathan (1997)
    <doi:10.1111/j.1540-6261.1997.tb04813.x> distance.
    The functions for testing model identification 
    specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> 
    and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test 
    to the regression coefficient matrix of test asset returns on risk factors.
    Finally, the function for heteroskedasticity and autocorrelation robust 
    covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912>
    covariance estimator.

Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics – A novel and practical method for estimating the “intrinsic” value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>.