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AIUQ: Ab Initio Uncertainty Quantification

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>.

Version: 0.5.3
Imports: fftwtools (≥ 0.9.11), SuperGauss (≥ 2.0.3), methods, plot3D (≥ 1.4)
Suggests: knitr, rmarkdown
Published: 2024-07-02
DOI: 10.32614/CRAN.package.AIUQ
Author: Yue He [aut], Xubo Liu [aut], Mengyang Gu [aut, cre]
Maintainer: Mengyang Gu <mengyang at pstat.ucsb.edu>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: AIUQ results

Documentation:

Reference manual: AIUQ.pdf
Vignettes: AIUQ tutorial

Downloads:

Package source: AIUQ_0.5.3.tar.gz
Windows binaries: r-devel: AIUQ_0.5.3.zip, r-release: AIUQ_0.5.3.zip, r-oldrel: AIUQ_0.5.3.zip
macOS binaries: r-release (arm64): AIUQ_0.5.3.tgz, r-oldrel (arm64): AIUQ_0.5.3.tgz, r-release (x86_64): AIUQ_0.5.3.tgz, r-oldrel (x86_64): AIUQ_0.5.3.tgz
Old sources: AIUQ archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=AIUQ to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.