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BIG-S2/GFPLVCM

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1
Language
R
Created over 5 years ago
Updated over 5 years ago

BIG-S2/GFPLVCM

BIG-S2

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CODE for paper “Generalized functional partial linear varying-coefficient model for asynchronous longitudinal data”

GWAS

GWAS Summary Statistics for Brain Imaging Phenotypes

L2RM

BSOINN-old-expired

Bayesian Scalar on Image Regression with Non-ignorable Non-response

TPRM

TPRM: Tensor partition regression models with applications in imaging biomarker detection

PSC

MWPCR

MWPCR stands for Multiscale Weighted Principal Component Regression. Please refer to the paper "MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction" for more details about the methods and models.

FSEM

Functional structural equation model for twin functional data

BSOINN

Bayesian Scalar on Image Regression with Non-ignorable Non-response

BCORSIS

SCALNET

RATS

Codes for paper "A robust adaptive two sample test in high dimensions"

SVCM

GENV

This is a package for fitting the groupwise envelope model

GEM

Copied from https://github.com/mlzxzhou/GEM

MFSDA_Python

Multivariate Functional Shape Data Analysis in Python (MFSDA_Python) is a Python based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical, biological variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.