Covariance is of universal prevalence across various disciplines within statistics.
We provide a rich collection of geometric and inferential tools for convenient analysis of
covariance structures, topics including distance measures, mean covariance estimator,
covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
For an introduction to covariance in multivariate statistical analysis,
see Schervish (1987)
|Author||Kyoungjae Lee [aut], Lizhen Lin [ctb], Kisung You [aut, cre]|
|Date of publication||2017-11-15 23:17:32 UTC|
|Maintainer||Kisung You <[email protected]>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.