divide_sdp

<p>Approximate a covariance matrix by a block diagonal matrix with blocks
of approximately equal size using Ward's method for hierarchical clustering</p>

internal

Model-free knockoffs are a general procedure for controlling the false
discovery rate (FDR) when performing variable selection. For more information,
see the website below and the accompanying paper: Candes et al.,
"Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection",
2016, <arXiv:1610.02351>.

Matteo Sesia

MFKnockoffs

Model-Free Knockoff Filter for Controlled Variable Selection

Rina Foygel Barber

Emmanuel Candes

Lucas Janson

Evan Patterson

divide_sdp function

Approximate a covariance matrix by a block diagonal matrix with blocks
of approximately equal size using Ward's method for hierarchical clustering — divide_sdp

Approximate a covariance matrix by a block diagonal matrix with blocks
of approximately equal size using Ward's method for hierarchical clustering

divide_sdp: Approximate a covariance matrix by a block diagonal matrix with blocks of approximately equal size using Ward's method for hierarchical clustering

Description

Usage

Arguments