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R_buildignore/developer_commands.R

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@@ -37,8 +37,12 @@ devtools::check() # run_dont_test = TRUE
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rcmdcheck::rcmdcheck() # build_args = "--run-donttest"
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devtools::build()
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#devtools::revdep(pkg = "fitHeavyTail") # to check reverse dependencies
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#devtools::check_win_release() #to check under windows
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#devtools::check_win_release() # to check under Windows
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#devtools::check_mac_release() # to check under Mac OS
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#rhub::check_for_cran(platform = "macos-highsierra-release-cran")
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#R CMD build . # this is to generate tarball
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#R CMD check fitHeavyTail_0.2.0.tar.gz --as-cran --run-donttest # this is before submission to CRAN
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#R CMD install fitHeavyTail_0.2.0.tar.gz
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#submit the tarball directly via the webform: https://cran.r-project.org/submit.html
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inst/CITATION

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citHeader("To cite 'fitHeavyTail' in publications, please use:")
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bibentry(entry = "Manual",
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bibentry(bibtype = "Manual",
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title = "{fitHeavyTail: Mean and Covariance Matrix Estimation under Heavy Tails}",
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author = c(as.person("D. P. Palomar"),
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as.person("R. Zhou"),
@@ -17,7 +17,7 @@ bibentry(entry = "Manual",
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"https://CRAN.R-project.org/package=fitHeavyTail")
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)
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bibentry(entry = "Article",
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bibentry(bibtype = "Article",
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title = "Regularized Tyler’s scatter estimator: Existence, uniqueness, and algorithms",
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author = c(as.person("Y. Sun"),
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as.person("P. Babu"),
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"https://doi.org/10.1109/TSP.2014.2348944")
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)
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bibentry(entry = "Article",
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bibentry(bibtype = "Article",
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title = "Regularized robust estimation of mean and covariance matrix under heavy-tailed distributions",
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author = c(as.person("Y. Sun"),
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as.person("P. Babu"),
@@ -55,7 +55,7 @@ bibentry(entry = "Article",
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"https://doi.org/10.1109/TSP.2015.2417513")
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)
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bibentry(entry = "Article",
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bibentry(bibtype = "Article",
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title = "Shrinking the Eigenvalues of M-estimators of Covariance Matrix",
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author = c(as.person("E. Ollila"),
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as.person("D. P. Palomar"),

vignettes/CovarianceEstimationHeavyTail.Rmd

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@@ -124,7 +124,7 @@ sum((fitted_3factors$cov - Sigma_cov)^2)
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To get a visual idea of the robustness, we can plot the shapes of the covariance matrices (true and estimated ones) on two dimensions. Observe how the heavy-tailed estimation follows the true one more closely than the sample covariance matrix:
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```{r scatter-plots, echo=FALSE, fig.width=10, fig.height=6, out.width="100%"}
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```{r scatter-plots, echo=FALSE, fig.width=10, fig.height=6, out.width="90%"}
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# fig.cap="Sample covariance matrix vs robust estimator."
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library(mvtnorm)
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library(ellipse)

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