- using R Under development (unstable) (2024-08-02 r86966)
- using platform: x86_64-pc-linux-gnu
- R was compiled by gcc-14 (GCC) 14.2.0 GNU Fortran (GCC) 14.2.0
- running under: Fedora Linux 36 (Workstation Edition)
- using session charset: UTF-8
- using option ‘--no-stop-on-test-error’
- checking for file ‘clustrd/DESCRIPTION’ ... OK
- checking extension type ... Package
- this is package ‘clustrd’ version ‘1.4.0’
- checking package namespace information ... OK
- checking package dependencies ... OK
- checking if this is a source package ... OK
- checking if there is a namespace ... OK
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- checking for hidden files and directories ... OK
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- checking for sufficient/correct file permissions ... OK
- checking whether package ‘clustrd’ can be installed ... [30s/39s] OK See the install log for details.
- checking package directory ... OK
- checking DESCRIPTION meta-information ... OK
- checking top-level files ... OK
- checking for left-over files ... OK
- checking index information ... OK
- checking package subdirectories ... OK
- checking code files for non-ASCII characters ... OK
- checking R files for syntax errors ... OK
- checking whether the package can be loaded ... OK
- checking whether the package can be loaded with stated dependencies ... OK
- checking whether the package can be unloaded cleanly ... OK
- checking whether the namespace can be loaded with stated dependencies ... OK
- checking whether the namespace can be unloaded cleanly ... OK
- checking loading without being on the library search path ... OK
- checking use of S3 registration ... OK
- checking dependencies in R code ... OK
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- checking replacement functions ... OK
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- checking R code for possible problems ... [53s/66s] OK
- checking Rd files ... NOTE 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)} | ^
- checking Rd metadata ... OK
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- checking for missing documentation entries ... OK
- checking for code/documentation mismatches ... OK
- checking Rd \usage sections ... OK
- checking Rd contents ... OK
- checking for unstated dependencies in examples ... OK
- checking contents of ‘data’ directory ... OK
- checking data for non-ASCII characters ... OK
- checking data for ASCII and uncompressed saves ... OK
- checking examples ... [48s/60s] OK
- checking PDF version of manual ... [8s/15s] OK
- checking HTML version of manual ... OK
- checking for non-standard things in the check directory ... OK
- checking for detritus in the temp directory ... OK
- DONE

Status: 1 NOTE