This is the companion package of a PhD thesis entitled "Bases Giving Distances. A new paradigm for investigating functional data with applications for spectroscopy" by Timmermans (2012). See references for details and related publications. The core of the BAGIDIS methodology is a functional wavelet based semi-distance that has been introduced by Timmermans and von Sachs (2010, 2015) and Timmermans, Delsol and von Sachs (2013). This semi-distance allows for comparing curves with sharp local patterns that might not be well aligned from one curve to another. It is data-driven and highly adaptive to the curves being studied. Its main originality is its ability to consider simultaneously horizontal and vertical variations of patterns, which proofs highly useful when used together with clustering algorithms or visualization method. BAGIDIS is an acronym for BAsis GIving DIStances. The extension of BAGIDIS to image data relies on the same principles and has been described in Timmermans and Fryzlewicz (2012), Fryzlewicz and Timmermans (2015).
|Maintainer:||Catherine Timmermans <catherine.timmermans at uclouvain.be>|
|License:||GPL-2 | GPL-3 [expanded from: GPL]|
|CRAN checks:||Bagidis results|
|Windows binaries:||r-devel: Bagidis_1.0.zip, r-release: Bagidis_1.0.zip, r-oldrel: Bagidis_1.0.zip|
|OS X El Capitan binaries:||r-release: Bagidis_1.0.tgz|
|OS X Mavericks binaries:||r-oldrel: Bagidis_1.0.tgz|
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