Showing 3 of total 3 results (show query)
go-ski
clustra:Clustering Longitudinal Trajectories
Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation.
Maintained by George Ostrouchov. Last updated 15 days ago.
4.78 score 6 scripts
snoweye
MixfMRI:Mixture fMRI Clustering Analysis
Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) <doi:10.1002/hbm.26425>) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in 'inst/workflow/') is also implemented with high performance techniques.
Maintained by Wei-Chen Chen. Last updated 6 months ago.
2 stars 4.26 score 18 scripts
andrewthomasjones
SAGMM:Clustering via Stochastic Approximation and Gaussian Mixture Models
Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.
Maintained by Andrew T. Jones. Last updated 6 years ago.
2.70 score 3 scripts