person("Weksi","Budiaji", email = "budiaji@untirta.ac.id", role = c("aut","cre"))
The desirable Dietary Pattern (DDP)/ PPH score
measures the variety of food consumption. The (weighted) score is calculated
based on the type of food. This package is intended to calculate
the DDP/ PPH score that is faster than traditional method via
a manual calculation by BKP (2017) <http://bkp.pertanian.go.id/storage/app/uploads/public/5bf/ca9/06b/5bfca906bc654274163456.pdf> and is simpler than the nutrition survey
<http://www.nutrisurvey.de>. The database to create weights and baseline values
is the Indonesia national survey in 2017.

Diagonally Dominant Principal Component Analysis
Zheng Tracy Ke <zke@fas.harvard.edu>
Lingzhou Xue <lingzhou@psu.edu>
Fan Yang <fyang1@uchicago.edu>
Consider the problem of decomposing a large covariance matrix into a low rank matrix plus a diagonally dominant matrix. This problem is called Diagonally Dominant Principal Component Analysis (DD-PCA) in the reference Ke, Z., Xue, L. and Yang, F. (2019) <arXiv:1906.00051>. DD-PCA can be used in covariance matrix estimation and global detection in multiple testing. This package implements DD-PCA using both convex approach and non-convex approach; Convex approach refers to solving a convex relaxation of the original problem using Alternating Direction Method of Multipliers (ADMM), while non-convex approach resorts to an iterative projection algorithm. This package also implements two global testing methods proposed in the reference.

Tracy Ke [aut],
Lingzhou Xue [aut],
Fan Yang [aut, cre]
c(person("Tracy", "Ke", email="zke@fas.harvard.edu", role=c("aut")),
person("Lingzhou", "Xue", email="lzxue@psu.edu", role=c("aut")),
person("Fan", "Yang", email="fyang1@uchicago.edu", role=c("aut", "cre")))
Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>)  by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.