Software
BSPBSS: Bayesian Spatial Blind Source Separation
https://cran.r-project.org/web/packages/BSPBSS/index.html
Wu, B., Guo, Y. and Kang, J. (2022). Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process. Journal of the American Statistical Association (JASA) Theory and Methods, pp1-12.
LOCUS: A Low-Rank Decomposition of Brain Connectivity Matrices with Uniform Sparsity
https://cran.r-project.org/web/packages/LOCUS/index.html
Wang, Y.† and Guo, Y.* (2023). LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity. Annals of Applied Statistics, 17(2): 1307-1332.
MMM: A Multimodal Multilevel Model for fusing fMRI and dMRI
https://github.com/Emory-CBIS/MMM
Hu, Y.†, Zeydabadinezhad, M., Li, L. and Guo,Y.* (2022). A multimodal multilevel neuroimaging model for investigating brain connectome development. Journal of the American Statistical Association (JASA). Open Access.
https://github.com/Emory-CBIS/DICA
Wu,B. †, Pal, S., Kang,K. and Guo,Y.* (2021). Distributional Independent Component Analysis for Diverse Neuroimaging Modalities. Biometrics. Open Access.
DDT: A Matlab toolbox for the Difference Degree Test.
https://github.com/Emory-CBIS/DDT
Higgins, IA†, Kundu, S., Choi, KS., Mayberg, H., Guo, Y* (2019), A Differential Degree Test for Comparing Brain Networks. Human Brain Mapping, 1-19. https://doi.org/10.1002/hbm.24718.
HINT : A Matlab GUI-based Hierarchical INdependent component analysis Toolbox.
Downloading: HINT 2.0 at https://github.com/Emory-CBIS/HINT
A video tutorial by Josh Lukemire on Youtube: https://www.youtube.com/watch?v=tRl9UFbT3R4&t=723s
Wang, Y., and Guo, Y. (2019). A hierarchical independent component analysis model for longitudinal neuroimaging studies. NeuroImage, 189, 380-400. https://doi.org/10.1016/j.neuroimage.2018.12.024
Lukemire, J.† , Wang, Y., Verma, A. and Guo, Y* (2020). HINT: A Hierarchical Independent Component Analysis Toolbox for Investigating Brain Functional Networks using Neuroimaging Data. Journal of Neuroscience Methods. 341: 108726. DOI. arXiv
Shi, R and Guo, Y . (2016). Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis. Annals of Applied Statistics. 10(4): 1930-1957.
DensParcorr: an R package for Dens-Based Method for Partial Correlation Estimation in Large Scale Brain Networks.
https://cran.r-project.org/web/packages/DensParcorr/index.html.
Wang, Y, Kang J, Kemmer, PB and Guo, Y. (2016). An efficient and reliable statistical method for estimating functional connectivity in large-scale brain networks using partial correlation. Frontiers in Neuroscience. 10:123.
siGGM: an R package for structurally informed Gaussian Graphical Model.
https://github.com/Emory-CBIS/siGGM
Higgins, IA†, Kundu, S. and Guo, Y., (2018). Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge. NeuroImage, 181: 263-278.
NIRStat: an R package for Studying Near-Infrared Spectroscopy (NIRS) Time Series
https://cran.r-project.org/web/packages/NIRStat/index.html
Guo, Y, Wang Y., Marin, T., Easley, K., Patel, R.M., Josephson, C.D. (2018). Statistical Methods for Characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy (NIRS) in preterm infants. Statistical Methods in Medical Research. https://doi.org/10.1177/0962280218786302. PubMed
CCPD: a Matlab toolbox for Connectivity Change Point Detection.
https://github.com/Emory-CBIS/CCPD
Kundu, S., Ming, J., Pierce, J. McDowell, J. and Guo, Y , (2018), Estimating Dynamic Brain Functional Networks Using Multi-subject fMRI Data, NeuroImage, 183: 635-649.
nonpar.ccc.est: an R package for nonparametric estimation of Lin's concordance correlation coefficient (CCC) in the presence of censored observations.
http://web1.sph.emory.edu/users/yguo2/software.html
Guo Y and Manatunga AK (2007). Nonparametric estimation of the concordance correlation coefficient under univariate censoring. Biometrics, 63(1): 164-172.