* using log directory ‘/home/hornik/tmp/R.check/r-release-gcc/Work/PKGS/clustrd.Rcheck’
* using R version 4.4.2 (2024-10-31)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc-14 (Debian 14.2.0-16) 14.2.0
    GNU Fortran (Debian 14.2.0-16) 14.2.0
* running under: Debian GNU/Linux trixie/sid
* using session charset: UTF-8
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* this is package ‘clustrd’ version ‘1.4.0’
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See 'https://www.r-project.org/nosvn/R.check/r-release-linux-x86_64/clustrd-00install.html' for details.
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checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
    38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
       |                                                                                                                                                                                                                         ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
    38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
       |                                                                                                                                                                                                                                     ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
    40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
       |                                                                                 ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
    40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
       |                                                                                             ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
    40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
       |                                                                                                                                                       ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
    40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
       |                                                                                                                                                                    ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
    40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
       |                                                                                                                                                                                    ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
    40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
       |                                                                                                                                                                                                                                                                                                        ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
    53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
       |                             ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
    54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
       |                             ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
    37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
       |                                                                                                                                                                                                                                ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
    37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
       |                                                                                                                                                                                                                                            ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
    39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
       |                                                                                 ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
    39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
       |                                                                                             ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
    39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
       |                                                                                                                                                       ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
    39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
       |                                                                                                                                                                    ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
    41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
       |                                                                                                                                                    ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
    41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
       |                                                                                                                                                                    ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
    54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
       |                             ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
    55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
       |                             ^
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Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
global_bootclus 4.34  0.004   5.297
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* DONE
Status: 1 NOTE