New utility gvar.dataset.svd_diagnosis for determining SVD cuts for correlation matrices estimated from a collection of random samples. See the documentation page Case Study: Correlations and SVD Cuts for more information. A couple of minor fixes
This release fixes lingering problems with gvar.dataset.avg_data(dataset) when dataset is a dictiona...
The singular value decomposition, or SVD, has been studied in the past as a tool for detecting and u...
Minor fix in (again) gvar.dataset.avg_data to improve speed for large problems and to work around a ...
Improves heuristics and documentation for gvar.dataset.svd_diagnosis. Updates gvar.svd including new...
More efficient handling of large sparse covariance matrices in gvar.svd (see gvar.evalcov_blocks())....
Small upgrade to gvar.dataset.svd_diagnosis to allow for more flexible use. Fixes bug in gvar.datase...
gvar.regulate is a new tool for regulating singular correlation matrices. It also supports both SVD ...
Small update responding to a feature request: gvar.dataset.avg_data(dset) no longer discards data wh...
This volume is an outgrowth of the 2nd International Workshop on SVD and Signal Processing which was...
<p>Variable coefficients derived from SVD analysis of the reduced set of variables in the explorator...
Adds new routines to gvar.linalg: eigh, svd, and lstsq. Adds YAML as an option, in addition to pickl...
Bug fix for gvar.gvar to remove problems caused when a GVar had exactly 0 standard deviation. Minor ...
Generalized canonical correlation analysis (GCANO) is a versatile technique that allows the joint an...
<p>SVD trends in raw (left panel) and normalized (right panel) clinical study data. Percentage at th...
Fixes issue with str(GVar) that was dropping significant digits in the mean when the standard deviat...
This release fixes lingering problems with gvar.dataset.avg_data(dataset) when dataset is a dictiona...
The singular value decomposition, or SVD, has been studied in the past as a tool for detecting and u...
Minor fix in (again) gvar.dataset.avg_data to improve speed for large problems and to work around a ...
Improves heuristics and documentation for gvar.dataset.svd_diagnosis. Updates gvar.svd including new...
More efficient handling of large sparse covariance matrices in gvar.svd (see gvar.evalcov_blocks())....
Small upgrade to gvar.dataset.svd_diagnosis to allow for more flexible use. Fixes bug in gvar.datase...
gvar.regulate is a new tool for regulating singular correlation matrices. It also supports both SVD ...
Small update responding to a feature request: gvar.dataset.avg_data(dset) no longer discards data wh...
This volume is an outgrowth of the 2nd International Workshop on SVD and Signal Processing which was...
<p>Variable coefficients derived from SVD analysis of the reduced set of variables in the explorator...
Adds new routines to gvar.linalg: eigh, svd, and lstsq. Adds YAML as an option, in addition to pickl...
Bug fix for gvar.gvar to remove problems caused when a GVar had exactly 0 standard deviation. Minor ...
Generalized canonical correlation analysis (GCANO) is a versatile technique that allows the joint an...
<p>SVD trends in raw (left panel) and normalized (right panel) clinical study data. Percentage at th...
Fixes issue with str(GVar) that was dropping significant digits in the mean when the standard deviat...
This release fixes lingering problems with gvar.dataset.avg_data(dataset) when dataset is a dictiona...
The singular value decomposition, or SVD, has been studied in the past as a tool for detecting and u...
Minor fix in (again) gvar.dataset.avg_data to improve speed for large problems and to work around a ...
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