Pinned Repositories
BCORSIS
BSOINN
Bayesian Scalar on Image Regression with Non-ignorable Non-response
BSOINN-old-expired
Bayesian Scalar on Image Regression with Non-ignorable Non-response
FSEM
Functional structural equation model for twin functional data
GEM
Copied from https://github.com/mlzxzhou/GEM
GWAS
GWAS Summary Statistics for Brain Imaging Phenotypes
keras-gnm
L2RM
LESA
PSC
BIG-S2's Repositories
BIG-S2/GWAS
GWAS Summary Statistics for Brain Imaging Phenotypes
BIG-S2/L2RM
BIG-S2/keras-gnm
BIG-S2/BSOINN-old-expired
Bayesian Scalar on Image Regression with Non-ignorable Non-response
BIG-S2/BSOINN
Bayesian Scalar on Image Regression with Non-ignorable Non-response
BIG-S2/GEM
Copied from https://github.com/mlzxzhou/GEM
BIG-S2/LESA
BIG-S2/Causal-Deepsets
Implementation for the paper "Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences" in Python.
BIG-S2/CQSTVCM
BIG-S2/FHFRM
Functional Hybrid Factor Regression Model
BIG-S2/FPLSDC
BIG-S2/GFPLVCM
CODE for paper “Generalized functional partial linear varying-coefficient model for asynchronous longitudinal data”
BIG-S2/PFLR_RKHS_code
BIG-S2/RATS
Codes for paper "A robust adaptive two sample test in high dimensions"
BIG-S2/SVCM
BIG-S2/CBIG
BIG-S2/D-CCA
D-CCA: A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets.
BIG-S2/DADP
BIG-S2/FMLR
BIG-S2/FNMEM
Functional nonlinear mixed effects models
BIG-S2/ldsc
LD Score Regression (LDSC)
BIG-S2/MFSDA
Multivariate Functional Shape Data Analysis (MFSDA) is a Matlab 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 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.
BIG-S2/notes
BIG-S2/OAI
Scripts for OAI project
BIG-S2/scilpy
The Sherbrooke Connectivity Imaging Lab (SCIL) Python dMRI processing toolbox
BIG-S2/SDM
Statistical Disease Mapping
BIG-S2/STVCM
BIG-S2/SVC
BIG-S2/SVCMEM
SVCMEM (Spatially Varying Coefficient Mixed Effects Model)
BIG-S2/TS2WM