{"id":2873,"date":"2018-09-16T19:47:35","date_gmt":"2018-09-16T23:47:35","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2?page_id=2873&preview_id=2873"},"modified":"2024-01-10T08:51:02","modified_gmt":"2024-01-10T13:51:02","slug":"software","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/software\/","title":{"rendered":"Software"},"content":{"rendered":"

BIG-S2 GitHub<\/a><\/p>\n

    \n
  1. TS2WM<\/a>: integration of brain tumor segmentation and GBM tumor impact on the WM integrity<\/li>\n
  2. FHFRM<\/a>: Functional Hybrid Factor Regression Model<\/li>\n
  3. keras-gnm<\/a>: Keras implementation of the Graph-based joint model with Nonignorable Missingness (GNM)<\/li>\n
  4. L2RM<\/a>: Low-rank Linear Regression Model<\/li>\n
  5. BSOINN<\/a>: Bayesian Scalar on Image Regression with Non-ignorable Non-response<\/li>\n
  6. SDM<\/a>: Statistical Disease Mapping<\/li>\n
  7. GFPLVCM<\/a>: Generalized Functional Partial Linear Varying-Coefficient Model for asynchronous longitudinal data<\/li>\n
  8. D-CCA<\/a>: A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets<\/li>\n
  9. SVCM<\/a>: Spatially Varying Coefficient Model, for association between massive imaging data and a set of covariates of interest<\/li>\n
  10. FSEM<\/a>: Functional Structural Equation Models for Twin Functional Data<\/li>\n
  11. PSC<\/a>: simultaneously characterize a large number of white matter bundles within and across different subjects for group analysis [link2<\/a>]<\/li>\n
  12. MonFuncReg<\/a>: Monotone Nonparametric Regression for Functional\/Longitudinal Data<\/li>\n
  13. SMAC<\/a>: Spatial Multi-category Angle-based Classifier<\/li>\n
  14. MILFM<\/a>: Multiple-Index Latent Factor Model for clinical outcome prediction based on massive features<\/li>\n
  15. SSPM<\/a>: Spatial Statistical Parametric Mapping (includes MAGEE, FADTTS and FMPM)<\/li>\n
  16. MGLREG<\/a>: analysis of secondary phenotypes collected in multiple group association studies<\/li>\n
  17. FGWAS<\/a>: Functional Genome Wide Association analysiS for imaging genetic analysis<\/li>\n
  18. TPRM<\/a>: Tensor Partition Regression Models with applications in imaging biomarker detection<\/li>\n
  19. MFSDA<\/a>: association testing between the multivariate shape measurements and demographic\/clinical variables [python version<\/a>]<\/li>\n
  20. L2R2<\/a>:\u00a0Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies [R version<\/a>]<\/li>\n
  21. SpatialACE<\/a>: estimate the spatial additive genetic, common environmental, and unique environmental model for cortical surface data<\/li>\n
  22. BLGRM<\/a>: Bayesian Row-rank Graph Regression Models for matrix response data<\/li>\n
  23. MWPCR<\/a>: Multiscale Weighted Principal Component Regression<\/li>\n
  24. BCor-SIS<\/a>: Ball Correlation Sure Independence Screening<\/li>\n
  25. RMRSS<\/a>:\u00a0Regression Models on Riemannian Symmetric Spaces<\/li>\n
  26. SCD<\/a>:\u00a0Perturbation and Scaled Cook\u2019s Distance<\/li>\n
  27. SSEM<\/a>:\u00a0Bayesian Lasso for Semiparametric Structural Equation Models Toolkit<\/li>\n
  28. HECD<\/a>: Pipeline for H&E image preprocessing and cell detection<\/li>\n
  29. MARM<\/a>:\u00a0DTI-Statistics “multiscale adaptive regression model (MARM)”\u00a0Toolkit<\/li>\n
  30. MAGEE-SSPM<\/a>:\u00a0Multiscale Adaptive Generalized Estimating Equation – Spatial Statistical Parametric Mapping<\/li>\n
  31. GENV<\/a>:\u00a0Groupwise Envelope model<\/li>\n
  32. GHMM<\/a>:\u00a0Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data<\/li>\n
  33. FVGWAS<\/a>:\u00a0Fast Voxelwise Genome Wide Association Analysis<\/li>\n
  34. LocalSPD<\/a>:\u00a0Local Polynomial Regression for Symmetric Positive\u00a0Definite Matrices<\/li>\n
  35. FADTTS<\/a>:\u00a0DTI-Statistics\u00a0functional analysis pipeline\u00a0Toolkit<\/li>\n
  36. VCDF<\/a>:\u00a0Varying Coefficient Model For Modeling Diffusion Tensors Along White Matter Tracts<\/li>\n
  37. SCALNET<\/a>: Scalable network estimation with L0 penalty for ultra-large Gaussian graphical models<\/li>\n
  38. MONFP<\/a>:\u00a0Clustering High-Dimensional Landmark-Based Two-Dimensional Shape Data<\/li>\n
  39. FMPM<\/a>:\u00a0Functional Mixed Processes Models<\/li>\n
  40. FRESMEM<\/a>:\u00a0Fixed and Random Effects Selection in Mixed Effects Toolkit<\/li>\n
  41. FRATS<\/a>:\u00a0Functional Regression Analysis of DTI Tract Statistics<\/li>\n
  42. PRM-MIP<\/a>:\u00a0Projection Regression Models for Multivariate Imaging Phenotype<\/li>\n<\/ol>\n
    \n
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    \n
    \n
    \n

    Software Agreement:<\/em><\/span><\/strong><\/p>\n

    THE SOFTWARE PACKAGES ARE PROVIDED “AS IS”, AND ONLY FOR\u00a0NON-PROFIT USE. CURRENTLY THERE IS NO FORMAL SUPPORT ON IT; FURTHER\u00a0ASSISTANCE BY THE AUTHORS REGARDING APPLICATION OF THIS SOFTWARE WILL NOT\u00a0BE PROVIDED, IN GENERAL. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY\u00a0DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\u00a0(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\u00a0SERVICES; LOSS OF USE, DATA ETC.) CAUSED AND ON ANY THEORY OF LIABILITY\u00a0ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE\u00a0PACKAGE.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

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