Last revisions on v.0.1.1

Author: dppalomar

.Rbuildignore

@@ -1,6 +1,7 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^cran-comments\.md$
+^LICENSE$
 ^README\.Rmd$
 ^README\.html$
 ^README_cache$

DESCRIPTION

@@ -1,19 +1,33 @@
 Package: fitHeavyTail
 Title: Mean and Covariance Matrix Estimation under Heavy Tails
 Version: 0.1.1
-Date: 2019-11-16
+Date: 2019-11-22
 Description: Robust estimation methods for the mean vector and covariance matrix 
     from data (possibly containing NAs) under multivariate heavy-tailed 
     distributions such as angular Gaussian, Cauchy, and Student's t. Additionally,
     a factor model structure can be specified for the covariance matrix.
+    The package is based on the papers:
+    Y. Sun, P. Babu, and D. P. Palomar (2014). Regularized Tyler’s Scatter 
+    Estimator: Existence, Uniqueness, and Algorithms. IEEE Trans. on Signal 
+    Processing, vol. 62, no. 19, pp. 5143-5156. <doi:10.1109/TSP.2014.2348944>.
+    Y. Sun, P. Babu, and D. P. Palomar (2015). Regularized Robust Estimation of 
+    Mean and Covariance Matrix Under Heavy-Tailed Distributions. IEEE Trans. on 
+    Signal Processing, vol. 63, no. 12, pp. 3096-3109. <doi:10.1109/SAM.2014.6882356>.
+    Chuanhai Liu and Donald B. Rubin (1995).
+    ML estimation of the t-distribution using EM and its extensions, ECM and ECME.
+    Statistica Sinica (5), pp. 19-39.
+    <https:www3.stat.sinica.edu.tw/statistica/oldpdf/A5n12.pdf>.
+    R. Zhou, J. Liu, S. Kumar, and D. P. Palomar (2015).
+    Robust factor analysis parameter estimation.
+    Lecture Notes in Computer Science (LNCS). <arXiv:1909.12530>.
 Authors@R: c(
   person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "[email protected]"),
   person("Rui", "Zhou", role =  "aut", email = "[email protected]")
   )
 Maintainer: Daniel P. Palomar <[email protected]>
 URL: https://github.com/dppalomar/fitHeavyTail
 BugReports: https://github.com/dppalomar/fitHeavyTail/issues
-License: GPL-3 | file LICENSE
+License: GPL-3
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 6.1.1

NEWS.md

@@ -1,4 +1,4 @@
-## Changes in fitHeavyTail version 0.1.1 (2019-11-16)
+## Changes in fitHeavyTail version 0.1.1 (2019-11-22)
 
 * Initial release is on CRAN.
 

README.Rmd

@@ -1,11 +1,11 @@
 ---
 always_allow_html: yes
 output:
-  md_document:
-    variant: markdown_github
   html_document:
     keep_md: yes
     variant: markdown_github
+  md_document:
+    variant: markdown_github
 ---
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
@@ -27,11 +27,9 @@ knit_hooks$set(pngquant = hook_pngquant)
 
 
 # fitHeavyTail
-<!---
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/fitHeavyTail)](https://CRAN.R-project.org/package=fitHeavyTail)
 [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/fitHeavyTail)](https://CRAN.R-project.org/package=fitHeavyTail)
 [![CRAN Downloads Total](https://cranlogs.r-pkg.org/badges/grand-total/fitHeavyTail?color=brightgreen)](https://CRAN.R-project.org/package=fitHeavyTail)
---->
 
 Robust estimation methods for the mean vector and covariance matrix 
 from data (possibly containing NAs) under multivariate heavy-tailed 
@@ -121,17 +119,17 @@ ggplot(data.frame(x = X[, i1], y = X[, i2]), aes(x, y)) +
 ```
 
 
-<!---
+
 ## Documentation
 For more detailed information, please check the
 [vignette](https://CRAN.R-project.org/package=fitHeavyTail/vignettes/CovarianceEstimationHeavyTail.html).
---->
+
 
 
 ## Links
 Package: [CRAN](https://CRAN.R-project.org/package=fitHeavyTail) and [GitHub](https://github.com/dppalomar/fitHeavyTail).
 
 README file: [GitHub-readme](https://github.com/dppalomar/fitHeavyTail/blob/master/README.md).
-<!---
+
 Vignette: [CRAN-vignette](https://CRAN.R-project.org/package=fitHeavyTail/vignettes/CovarianceEstimationHeavyTail.html).
---->
+

README.html

@@ -1,28 +1,34 @@
+---
+always_allow_html: yes
+output:
+  html_document:
+    keep_md: yes
+    variant: markdown_github
+  md_document:
+    variant: markdown_github
+---
+
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-fitHeavyTail
-============
 
-<!---
+
+
+
+# fitHeavyTail
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/fitHeavyTail)](https://CRAN.R-project.org/package=fitHeavyTail)
 [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/fitHeavyTail)](https://CRAN.R-project.org/package=fitHeavyTail)
 [![CRAN Downloads Total](https://cranlogs.r-pkg.org/badges/grand-total/fitHeavyTail?color=brightgreen)](https://CRAN.R-project.org/package=fitHeavyTail)
---->
-
-Robust estimation methods for the mean vector and covariance matrix from
-data (possibly containing NAs) under multivariate heavy-tailed
-distributions such as angular Gaussian, Cauchy, and Student’s *t*.
-Additionally, a factor model structure can be specified for the
-covariance matrix.
 
-Installation
-------------
+Robust estimation methods for the mean vector and covariance matrix 
+from data (possibly containing NAs) under multivariate heavy-tailed 
+distributions such as angular Gaussian, Cauchy, and Student's $t$. Additionally,
+a factor model structure can be specified for the covariance matrix.
+    
 
-The package can be installed from
-[CRAN](https://CRAN.R-project.org/package=fitHeavyTail) or
-[GitHub](https://github.com/dppalomar/fitHeavyTail):
+## Installation
+The package can be installed from [CRAN](https://CRAN.R-project.org/package=fitHeavyTail) or [GitHub](https://github.com/dppalomar/fitHeavyTail):
 
-``` r
+```r
 # install stable version from CRAN
 install.packages("fitHeavyTail")
 
@@ -32,26 +38,23 @@ devtools::install_github("dppalomar/fitHeavyTail")
 
 To get help:
 
-``` r
+```r
 library(fitHeavyTail)
 help(package = "fitHeavyTail")
 ?fit_mvt
 ```
 
 To cite `fitHeavyTail` in publications:
 
-``` r
+```r
 citation("fitHeavyTail")
 ```
 
-Quick Start
------------
 
-To illustrate the simple usage of the package `fitHeavyTail`, let’s
-start by generating some multivariate data under a Student’s *t*
-distribution with significant heavy tails (*ν* = 4):
+## Quick Start
+To illustrate the simple usage of the package `fitHeavyTail`, let's start by generating some multivariate data under a Student's $t$ distribution with significant heavy tails ($\nu=4$):
 
-``` r
+```r
 library(mvtnorm)  # package for multivariate t distribution
 N <- 10   # number of variables
 T <- 80   # number of observations
@@ -65,25 +68,23 @@ Sigma_scatter <- (nu-2)/nu * Sigma
 X <- rmvt(n = T, delta = mu, sigma = Sigma_scatter, df = nu)  # generate data
 ```
 
-We can first estimate the mean vector and covariance matrix via the
-traditional sample estimates (i.e., sample mean and sample covariance
-matrix):
+We can first estimate the mean vector and covariance matrix via the traditional sample estimates (i.e., sample mean and sample covariance matrix):
 
-``` r
+```r
 mu_sm <- colMeans(X)
 Sigma_scm <- cov(X)
 ```
 
 Then we can compute the robust estimates via the package `fitHeavyTail`:
 
-``` r
+```r
 library(fitHeavyTail)
 fitted <- fit_mvt(X)
 ```
 
 We can now compute the estimation errors and see the big improvement:
 
-``` r
+```r
 sum((mu_sm - mu)^2)
 #> [1] 0.2857323
 sum((fitted$mu - mu)^2)
@@ -95,26 +96,21 @@ sum((fitted$cov - Sigma)^2)
 #> [1] 2.957443
 ```
 
-To get a visual idea of the robustness, we can plot the shapes of the
-covariance matrices (true and estimated ones) projected on two
-dimensions. Observe how the heavy-tailed estimation follows the true one
-more closely than the sample covariance matrix:
+To get a visual idea of the robustness, we can plot the shapes of the covariance matrices (true and estimated ones) projected on two dimensions. Observe how the heavy-tailed estimation follows the true one more closely than the sample covariance matrix:
 <img src="man/figures/README-ellipses-1.png" width="80%" style="display: block; margin: auto;" />
 
-<!---
+
+
 ## Documentation
 For more detailed information, please check the
 [vignette](https://CRAN.R-project.org/package=fitHeavyTail/vignettes/CovarianceEstimationHeavyTail.html).
---->
 
-Links
------
 
-Package: [CRAN](https://CRAN.R-project.org/package=fitHeavyTail) and
-[GitHub](https://github.com/dppalomar/fitHeavyTail).
 
-README file:
-[GitHub-readme](https://github.com/dppalomar/fitHeavyTail/blob/master/README.md).
-<!---
+## Links
+Package: [CRAN](https://CRAN.R-project.org/package=fitHeavyTail) and [GitHub](https://github.com/dppalomar/fitHeavyTail).
+
+README file: [GitHub-readme](https://github.com/dppalomar/fitHeavyTail/blob/master/README.md).
+
 Vignette: [CRAN-vignette](https://CRAN.R-project.org/package=fitHeavyTail/vignettes/CovarianceEstimationHeavyTail.html).
---->
+

README.md

@@ -1,4 +1,4 @@
-First release in CRAN, version 0.1.1 (2019-11-16), (package also available in GitHub).
+First release in CRAN, version 0.1.1 (2019-11-22), (package also available in GitHub).
 
 ---
 

cran-comments.md

@@ -57,7 +57,10 @@ test_that("factor structure constraint on scatter matrix works", {
   # save(mvt_model_factor_check, file = "fitted_mvt_factor_check.RData", version = 2, compress = "xz")
   mvt_model_factor <- fit_mvt(X, factors = 3)
   load("fitted_mvt_factor_check.RData")
-  expect_equal(mvt_model_factor, mvt_model_factor_check)
+  expect_equal(mvt_model_factor[c("mu", "cov", "scatter", "nu", "psi", "converged")],
+               mvt_model_factor_check[c("mu", "cov", "scatter", "nu", "psi", "converged")])
+  expect_equal(mvt_model_factor$B %*% t(mvt_model_factor$B),
+               mvt_model_factor_check$B %*% t(mvt_model_factor_check$B))
 })
 
 

tests/testthat/test-fit_mvt.R

@@ -41,7 +41,7 @@ knit_hooks$set(pngquant = hook_pngquant)
 ```
 
 -----------
-> This vignette illustrates the usage of the package [`portfolioBacktest`](https://CRAN.R-project.org/package=fitHeavyTail) 
+> This vignette illustrates the usage of the package [`fitHeavyTail`](https://CRAN.R-project.org/package=fitHeavyTail) 
 to estimate the mean vector and covariance matrix of heavy-tailed multivariate distributions such as the angular Gaussian, Cauchy, or Student's $t$ distribution. The results are compared against existing benchmark functions from different packages.
 
 
@@ -87,7 +87,6 @@ Sigma_scm <- cov(X)
 Then we can compute the robust estimates via the package `fitHeavyTail`:
 ```{r}
 library(fitHeavyTail)
-
 fitted <- fit_mvt(X)
 ```
 

vignettes/CovarianceEstimationHeavyTail.Rmd

@@ -11,7 +11,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1">
 
 
-<meta name="date" content="2019-11-15" />
+<meta name="date" content="2019-11-22" />
 
 <title>Mean Vector and Covariance Matrix Estimation under Heavy Tails</title>
 
@@ -113,7 +113,7 @@
 <h1 class="title toc-ignore">Mean Vector and Covariance Matrix Estimation under Heavy Tails</h1>
 <h3 class="author"><div class="line-block">Daniel P. Palomar and Rui ZHOU<br />
 The Hong Kong University of Science and Technology (HKUST)</div></h3>
-<h3 class="date">2019-11-15</h3>
+<h3 class="date">2019-11-22</h3>
 </header>
 
 <hr>
@@ -131,7 +131,7 @@ <h3 class="date">2019-11-15</h3>
 
 <hr />
 <blockquote>
-<p>This vignette illustrates the usage of the package <a href="https://CRAN.R-project.org/package=fitHeavyTail"><code>portfolioBacktest</code></a> to estimate the mean vector and covariance matrix of heavy-tailed multivariate distributions such as the angular Gaussian, Cauchy, or Student’s <span class="math inline">\(t\)</span> distribution. The results are compared against existing benchmark functions from different packages.</p>
+<p>This vignette illustrates the usage of the package <a href="https://CRAN.R-project.org/package=fitHeavyTail"><code>fitHeavyTail</code></a> to estimate the mean vector and covariance matrix of heavy-tailed multivariate distributions such as the angular Gaussian, Cauchy, or Student’s <span class="math inline">\(t\)</span> distribution. The results are compared against existing benchmark functions from different packages.</p>
 </blockquote>
 <div id="installation" class="section level1">
 <h1>Installation</h1>
@@ -166,8 +166,7 @@ <h1>Quick Start</h1>
 <a class="sourceLine" id="cb3-2" title="2">Sigma_scm &lt;-<span class="st"> </span><span class="kw">cov</span>(X)</a></code></pre></div>
 <p>Then we can compute the robust estimates via the package <code>fitHeavyTail</code>:</p>
 <div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw">library</span>(fitHeavyTail)</a>
-<a class="sourceLine" id="cb4-2" title="2"></a>
-<a class="sourceLine" id="cb4-3" title="3">fitted &lt;-<span class="st"> </span><span class="kw">fit_mvt</span>(X)</a></code></pre></div>
+<a class="sourceLine" id="cb4-2" title="2">fitted &lt;-<span class="st"> </span><span class="kw">fit_mvt</span>(X)</a></code></pre></div>
 <p>We can now compute the estimation errors and see the big improvement:</p>
 <div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw">sum</span>((mu_sm <span class="op">-</span><span class="st"> </span>mu)<span class="op">^</span><span class="dv">2</span>)</a>
 <a class="sourceLine" id="cb5-2" title="2"><span class="co">#&gt; [1] 0.2857323</span></a>