NEWS.md
cvCovEst()
no longer accepts center
and scale
arguments. Data centering is now handled within each estimator function. Users no longer have an option to scale their data. If they’d like to produce a correlation matrix, they must scale the cvCovEst()
estimate using cov2cor()
..data$
in dplyr::select()
statements.spikedOperatorShrinkEst()
, spikedFrobeniusShrinkEst()
and spikedSteinShrinkEst()
, and apply the asymptotically optimal amount of shrinkage on the sample covariance matrix’s eigenvalues with respect to their respective loss functions. For more information on these estimators, see Donoho et al.’s Annals of Statistics article “Optimal Shrinkage of Eigenvalues in the Spiked Covariance Model”.summary()
now reports metrics about the candidate estimators’ estimates, like their condition numbers, signe, and sparsity levels.scadEst()
and adaptiveLassoEst()
are now vectorized, greatly improving their computational efficiency. Thanks, Brian!cvCovEst()
no longer accepts the true_cov_mat
argument.cvCovEst
is now ready for publication through JOSS.inst/REFERENCES.bib
pkgdown
documentationcvCovEst()
. This is the last version of cvCovEst
that can be used to reproduce the simulation results of the accompanying manuscript, “Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions”.summary.cvCovEst()
when a single summary function is specified now immediately returns a table instead of a list of length 1 that contains said table.summary.cvCovEst()
no longer have dplyr
groups.empirical_risk
column in risk_df
table output by cvCovEst()
to cv_risk
.robustPoetEst()
warning again its use for correlation matrix estimation.robustPoetEst
plots.cvCovEst
R package a public repository GitHub.cvCovEst
now possesses a slew of diagnostic and visualization tools. A detailed description of these functions will be added to the vignette in the near future.NEWS.md
, adding consistency in bullet point indicator and enforcing the 80-column rule.stringi
since only invoked in a single pipe call in checkArgs
.cvScaledMatrixFrobeniusLoss
, a new matrix-based loss function that scales squared error calculations associated with each entry of a covariance matrix estimate by the sample variances of the entry’s row and column variables. This is particularly useful if the features of your dataset are of different magnitude. It’s approximately equivalent to estimating the correlation matrix, but without the need to re-scale the estimated correlation matrix to be an estimated covariance matrix.denseLinearShrinkEst
: the shrinkage parameter was often selected such that the dense target was returned as the estimate.nlShrinkLWEst
by changing a conditional, as per https://github.com/PhilBoileau/cvCovEst/issues/23.tidyverse
code style via the first call to styler
in this codebase (via make style
).NEWS.md
.stats::cov
with coop::covar
after resolving the issue on Linux machines, as per https://github.com/PhilBoileau/cvCovEst/issues/18.cvCovEst
and from cvFrobeniusLoss
when the true covariance matrix is passed in.coop::covar
due to strange parallelization issue on Linux machines. Hopefully we can use it again one day.cvCovEst
if the estimator in questions doesn’t have any hyperparameters.cvCovEst
.