Oliver Y. Chén bio photo

Oliver Y. Chén

x


Packages

  • Chén O.Y. (Author and Maintainer). Generalized Populaton Value Decomposition (gPVD).

The package provides a general method for simultaneous dimensionality reduction of large populations of massive images. The input can be a massive matrix containing brain imaging data obtained from hundreds of thousands of subjects. The package contains two parts: (1) group-level information shared by a population and (2) subject-specific information that is idiosyncratic. The codes are based upon the generalized population value decomposition (gPVD) developed in by Chén et al. (see [Paper]), which was inspired by and extended the population value decomposition (PVD) method (cf. Crainiceanu et al. (2011) Population value decomposition, a framework for the analysis of image populations. JASA 106, no. 495 (2011): 775-790).

Please send me an email if you would like a copy of the package.


  • Chén O.Y. (Author and Maintainer). Spike Timing-Dependent Plasticity.

The software studies the visual and quantitative properties of different interactions and STDP (Spike Timing-Dependent Plasticity) learning window (see Software section of the [Document] for more information).

Package:

library(shiny)
runGitHub("oliverychen/pfister")


Additional software:

Server [server.R]
User interface [ui.R]


  • Chén O.Y. (Author and Maintainer). Behavior Predication via Brain Networks.


The package employs positive and negative brain network to predicate behavior via feature selection and leave-one-out cross-validation (LOOCV).

Please send me an email if you would like a copy of the package.


  • Chén O.Y. (Author and Maintainer). GIFTS: Generalized Iterative Feature Training and Selection.


Please send me an email if you would like a copy of the package.


  • Chén O.Y. (Author and Maintainer). PDM (Principal Direction of Mediation).


The package provides functions that calculate the estimates of the Principal Direction of Mediations (PDMs) and corresponding path coefficients of ultra-high dimensional data, provided treatment (e.g. thermal pain), response (e.g. reported pain), and mediation data (e.g. measurements of fMRI data).

Please send me an email if you would like a copy of the package.


  • Chén O.Y. (Co-author). Refund. I contributed functions that (1) perform parameter estimation for bivariate functions; and (2) conduct covariance estimation and smoothing.