covmat: Covariance Matrix Estimation

We implement a collection of techniques for estimating covariance matrices. Covariance matrices can be built using missing data. Stambaugh Estimation and FMMC methods can be used to construct such matrices. Covariance matrices can be built by denoising or shrinking the eigenvalues of a sample covariance matrix. Such techniques work by exploiting the tools in Random Matrix Theory to analyse the distribution of eigenvalues. Covariance matrices can also be built assuming that data has many underlying regimes. Each regime is allowed to follow a Dynamic Conditional Correlation model. Robust covariance matrices can be constructed by multivariate cleaning and smoothing of noisy data.

Package details

AuthorRohit Arora
MaintainerRohit Arora <>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the covmat package in your browser

Any scripts or data that you put into this service are public.

covmat documentation built on May 30, 2017, 4:36 a.m.