Description
Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.
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r-devel-linux-x86_64-debian-clang
CRAN incoming feasibility
Maintainer: ‘Eric Bridgeford <ericwb95@gmail.com>’ The Description field contains et al. (2017) <arXiv:1709.01233>, we provide several methods for Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTE
r-devel-linux-x86_64-debian-clang
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25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
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36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
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68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
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75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
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r-devel-linux-x86_64-debian-gcc
CRAN incoming feasibility
Maintainer: ‘Eric Bridgeford <ericwb95@gmail.com>’ The Description field contains et al. (2017) <arXiv:1709.01233>, we provide several methods for Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTE
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25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
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36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
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68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
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75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
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25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
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36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
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68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
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75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
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25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
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36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
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31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
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68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
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32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
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75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
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checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:24: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-devel-windows-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:24: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-oldrel-macos-arm64
LazyData
'LazyData' is specified without a 'data' directory
NOTE
r-oldrel-macos-arm64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:24: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-oldrel-macos-x86_64
LazyData
'LazyData' is specified without a 'data' directory
NOTE
r-oldrel-macos-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:24: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-oldrel-windows-x86_64
LazyData
'LazyData' is specified without a 'data' directory
NOTE
r-oldrel-windows-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:24: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-patched-linux-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:24: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-release-linux-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-release-macos-arm64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-release-macos-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
NOTE
r-release-windows-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.project.pca.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.sims.fat_tails.Rd:25: Lost braces; missing escapes or markup?
25 | \item{f}{the fatness scaling of the tail. S2 = f*S1, where S1_{ij} = rho if i != j, and 1 if i == j. Defaults to \code{15}.}
| ^
checkRd: (-1) lol.sims.mean_diff.Rd:36: Lost braces; missing escapes or markup?
36 | \item{s}{the scaling parameter of the covariance matrix. S_{ij} = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to \code{1}.}
| ^
checkRd: (-1) lol.utils.decomp.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:22: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:24: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.utils.decomp.Rd:25: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:31: Lost braces; missing escapes or markup?
31 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X}, \code{Y}, and has a parameter for \code{alg.dimname} if \code{alg} is supervised, or just \code{X} and \code{alg.dimname} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r <= d} dimensions.}
| ^
checkRd: (-1) lol.xval.eval.Rd:68: Lost braces
68 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:32: Lost braces; missing escapes or markup?
32 | \item{alg}{the algorithm to use for embedding. Should be a function that accepts inputs \code{X} and \code{Y} and embedding dimension \code{r} if \code{alg} is supervised, or just \code{X} and embedding dimension \code{r} if \code{alg} is unsupervised.This algorithm should return a list containing a matrix that embeds from {d} to {r < d} dimensions.}
| ^
checkRd: (-1) lol.xval.optimal_dimselect.Rd:75: Lost braces
75 | \item{if ]code{rank.low == TRUE}, users cross-validation method with \code{ntrain = min((k-1)/k*n, d)} sample training sets, where \code{d} is the number of dimensions in \code{X}. This ensures that the training data is always low-rank, \code{ntrain < d + 1}. Note that the resulting training sets may have \code{ntrain < (k-1)/k*n}, but the resulting testing sets will always be properly rotated \code{ntest = n/k} to ensure no dependencies in fold-wise testing.}
| ^
checkRd: (-1) predict.nearestCentroid.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.nearestCentroid.Rd:14: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomChance.Rd:13: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:12: Lost braces in \itemize; meant \describe ?
checkRd: (-1) predict.randomGuess.Rd:13: Lost braces in \itemize; meant \describe ?
Check History
NOTE 0 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE
r-devel-linux-x86_64-debian-clang
CRAN incoming feasibility
Maintainer: ‘Eric Bridgeford <ericwb95@gmail.com>’ The Description field contains et al. (2017) <arXiv:1709.01233>, we provide several methods for Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTE
r-devel-linux-x86_64-debian-gcc
CRAN incoming feasibility
Maintainer: ‘Eric Bridgeford <ericwb95@gmail.com>’ The Description field contains et al. (2017) <arXiv:1709.01233>, we provide several methods for Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTE
r-devel-linux-x86_64-fedora-clang
dependencies in R code
Namespace in Imports field not imported from: ‘ggplot2’ All declared Imports should be used.
NOTE
r-devel-linux-x86_64-fedora-gcc
dependencies in R code
Namespace in Imports field not imported from: ‘ggplot2’ All declared Imports should be used.
NOTE
r-devel-macos-arm64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-devel-windows-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-patched-linux-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-release-linux-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-release-macos-arm64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-release-macos-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-release-windows-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-oldrel-macos-arm64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-oldrel-macos-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
NOTE
r-oldrel-windows-x86_64
Rd files
checkRd: (-1) lol.project.lrlda.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.lrlda.Rd:22: Lost braces in \itemize; meant \describe ? checkRd: (-1) lol.project.pca.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (
Dependency Network
Version History
new
2.1
Mar 10, 2026