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• using platform: x86_64-w64-mingw32
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• running under: Windows Server 2022 x64 (build 20348)
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• this is package 'ExtremeRisks' version '0.0.4'
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• checking Rd files ... [1s] NOTE checkRd: (-1) estMultiExpectiles.Rd:30: Lost braces     30 | \item If \code{var=TRUE} then an estimate of the asymptotic variance-covariance matrix of the \code{d}-dimensional expecile estimator is computed. If the data are regarded as \code{d}-dimensional temporal independent observations coming from dependent variables. Then, the asymptotic variance-covariance matrix is estimated by the formulas in section 3.1 of Padoan and Stupfler (2020). In particular, the variance-covariance matrix is computed exploiting the asymptotic behaviour of the relative explectile estimator appropriately normalized and using a suitable adjustment. This is achieved through \code{varType="asym-Ind-Adj"}. The data can also be regarded as code{d}-dimensional temporal independent observations coming from independent variables. In this case the asymptotic variance-covariance matrix is diagonal and is also computed exploiting the formulas in section 3.1 of Padoan and Stupfler (2020). This is achieved through \code{varType="asym-Ind"}.        | ^ checkRd: (-1) predMultiExpectiles.Rd:33: Lost braces     33 | \item If \code{var=TRUE} then an estimate of the asymptotic variance-covariance matrix of the \eqn{tau'_n}-\emph{th} \code{d}-dimensional expectile is computed. Notice that the estimation of the asymptotic variance-covariance matrix \bold{is only available} when \eqn{\gamma} is estimated using the Hill estimator (see \link{MultiHTailIndex}). The data are regarded as temporal independent observations coming from dependent variables. The asymptotic variance-covariance matrix is estimated exploiting the formulas in Section 3.2 of Padoan and Stupfler (2020). The variance-covariance matrix is computed exploiting the asymptotic behaviour of the normalized expectile estimator which is expressed in logarithmic scale. In addition, a suitable adjustment is considered. This is achieved through \code{varType="asym-Ind-Adj-Log"}. The data can also be regarded as code{d}-dimensional temporal independent observations coming from independent variables. In this case the asymptotic variance-covariance matrix is diagonal and is also computed exploiting the formulas in Section 3.2 of Padoan and Stupfler (2020). This is achieved through \code{varType="asym-Ind-Log"}. If \code{varType="asym-Ind-Adj"}, then the variance-covariance matrix is computed exploiting the asymptotic behaviour of the relative expectile estimator appropriately normalized and exploiting a suitable adjustment. This concerns the case of dependent variables. The case of independent variables is achieved through \code{varType="asym-Ind"}.        | ^ checkRd: (-1) sp500.Rd:5: Escaped LaTeX specials: \&
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Status: 1 NOTE