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Commit5836a58

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Fix CRAN checks
1 parent95a7fc9 commit5836a58

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9 files changed

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‎R/condextr.R‎

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‎R/datasets.R‎

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#'
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#' Interview component of survey 'What we eat in
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#' America'. These are extracted from the 2015–2016 National Health and Nutrition Examination Survey (NHANES, \url{https://wwwn.cdc.gov/nchs/nhanes/Default.aspx}) report and consist of the total nutrients for all food and beverage intake ingested over a 24 hours period.
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#' @source National Center for Health Statistics, now available from the Wayback Machine via \url{https://web.archive.org/web/20201029113801/https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DR1TOT_I.XPT}
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=======
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#' @source National Center for Health Statistics, \url{https://web.archive.org/web/20201020150740/https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DR1TOT_I.XPT}
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>>>>>>> f7be225 (FixCRANdevel)
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#'
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#' @details Note that the sample design oversampled specific population targets and that only respondants are provided. The website contains more information about sampling weights. There are multiple missing records.
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#' @format A data frame with 9544 rows and 38 variables:
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#' \item{\code{sele}}{selenium (in micrograms)}
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#'}
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#' @name nutrients
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#' @note These data are subject to a data user agreement, available at \url{https://www.cdc.gov/nchs/policy/data-user-agreement.html}
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=======
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#' @note These data are subject to a data user agreement, see \url{https://www.cdc.gov/nchs/policy/data-user-agreement.html}
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>>>>>>> f7be225 (FixCRANdevel)
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"nutrients"
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‎R/pickands.R‎

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#'
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#' Restricting to the largest fourth of the data, returns the number of exceedances that minimizes the Kolmogorov-Smirnov statistic, i.e., the maximum absolute difference between the estimated generalized Pareto and the empirical distribution of exceedances. Relative to the paper, different estimation methods are proposed.
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#'
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#' @references James Pickands III (1975). \emph{Statistical inference using extreme order statistics}, Annals of Statistics, 3(\bold{1}) 119-131, \url{https://doi.org/10.1214/aos/1176343003}
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#' @references James Pickands III (1975). \emph{Statistical inference using extreme order statistics}, Annals of Statistics, 3(\bold{1}) 119-131, \doi{10.1214/aos/1176343003}
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#' @param xdat [numeric] vector of observations
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#' @param method [string] estimation method, either the quartiles of Pickands (1975), maximum likelihood, probability weighted moments or L-moments
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#' @return a vector with elements \code{k}, the number of order statistics to keep, and \code{thresh}, the numerical value of the latter
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#' @param thresh [numeric] vector of candidate thresholds. If missing, 20 sample quantiles starting at the 0.25 quantile in increments of 3.75 percent.
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#' @param plot [logical] if \code{TRUE}, return a plot of the sample L-kurtosis against the L-skewness, along with the theoretical generalized Pareto curve.
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#' @return scalar for the chosen numeric threshold
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#' @references Silva Lomba, J., Fraga Alves, M.I. (2020). \emph{L-moments for automatic threshold selection in extreme value analysis}. Stoch Environ Res Risk Assess, 34, 465–491. \url{https://doi.org/10.1007/s00477-020-01789-x}
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#' @references Silva Lomba, J., Fraga Alves, M.I. (2020). \emph{L-moments for automatic threshold selection in extreme value analysis}. Stoch Environ Res Risk Assess, 34, 465–491. \doi{10.1007/s00477-020-01789-x}
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#' @export
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thselect.alrs<-function(xdat,thresh,plot=FALSE) {
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xdat<- as.numeric(xdat[is.finite(xdat)])

‎R/thselect.R‎

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#' of Schneider et al. (2021) for threshold selection.
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#' The implementation uses a second-order regular variation index of -1
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#'
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#' @references Schneider, L.F., Krajina, A. & Krivobokova, T. (2021). \emph{Threshold selection in univariate extreme value analysis}, Extremes, \bold{24}, 881–913 \url{https://doi.org/10.1007/s10687-021-00405-7}
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#' @references Schneider, L.F., Krajina, A. & Krivobokova, T. (2021). \emph{Threshold selection in univariate extreme value analysis}, Extremes, \bold{24}, 881–913 \doi{10.1007/s10687-021-00405-7}
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#' @param xdat vector of positive exceedances
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#' @return a list with elements
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#' \describe{
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#' Minimum distance threshold selection procedure
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#'
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#'
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#' @references Clauset, A., Shalizi, C.R. and Newman, M.E.J. (2009). \emph{Power-Law Distributions in Empirical Data}. SIAM Review. Society for Industrial and Applied Mathematics, \bold{51}, 661-703, \url{https://doi.org/10.1137/070710111}
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#' @references Clauset, A., Shalizi, C.R. and Newman, M.E.J. (2009). \emph{Power-Law Distributions in Empirical Data}. SIAM Review. Society for Industrial and Applied Mathematics, \bold{51}, 661-703, \doi{10.1137/070710111}
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#' @param xdat vector of positive exceedances
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#' @return a list with components
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#' \describe{

‎man/nutrients.Rd‎

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‎man/thselect.alrs.Rd‎

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‎man/thselect.mdps.Rd‎

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‎man/thselect.pickands.Rd‎

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‎man/thselect.samsee.Rd‎

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