Authors@R: c(person("Jean-Eudes", "Dazard", role = c("aut", "cre"),
email = "jxd101@case.edu"), person("Hua", "Xu", role = "ctb",
email = "huaxu77@gmail.com"), person("Alberto", "Santana", role
Description: MVR is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm), among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom.
(i) Normalization and/or variance stabilization of the data,
(ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Depends: R (>= 2.15.0), parallel, statmod

Author: Jean-Eudes Dazard [aut, cre], Hua Xu [ctb], Alberto Santana [ctb]
Description: This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data.
It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm).
Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom.
Depends: R (>= 3.0.2), parallel, statmod

Imports: graphics, grDevices, methods, stats

Description: This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include: