R-universe - jedazard (Jean-Eudes Dazard, PhD, MS)

superpc - Supervised Principal Components

Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.

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7.42 score 8 stars 2 dependents 111 scripts 11k downloads

MVR - Mean-Variance Regularization

Implements 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: (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.

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cpp

3.88 score 1 stars 15 scripts 183 downloads