Maintainer: Clayton Vieira Fraga Filho <altoe.wagner@gmail.com>

Maintainer: Clayton Vieira Fraga Filho <forestgrowthsoftware@gmail.com>
Packaged: 2016-07-31 18:05:52 UTC; mfernandes

Packaged: 2016-08-07 19:03:32 UTC; mfernandes

grDevices, ReporteRs, methods, ggplot2, gridExtra
Packaged: 2016-09-26 18:31:43 UTC; mfernandes

Packaged: 2016-10-11 21:17:27 UTC; mfernandes

Packaged: 2016-09-09 17:25:11 UTC; mfernandes

Authors@R: c(person("Prashant", "Gupta", email = "prashant10991@gmail.com", role = c("aut", "cre")), person("Aashi", "Jindal", email = "jindal.aashi21@gmail.com", role = c("aut")), person("Jayadeva", email = "iitjd4@gmail.com", role = c("aut")) ,person("Debarka", "Sengupta", email = "debarka@gmail.com", role = c("aut")))
Description: The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets.
The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.
Note: While reporting issues on github start subject line with

Description: Proposes non-parametric estimates of the Fisher information measure and the
Shannon entropy power. The state-of-the-art studies related to the Fisher-Shannon
measures, with new analytical formulas for positive unimodal skewed distributions
are presented in Guignard et al. <arXiv:1912.02452>. A 'python' version of this
work is available on 'github' and 'PyPi' ('FiShPy').
Note: The authors are grateful to Mikhail Kanevski, Federico Amato and
Luciano Telesca for many fruitful discussions about the use and
the application of Fisher-Shannon method.

Shannon entropy power. More theoretical and implementation details can be found
in Guignard et al. <doi:10.3389/feart.2020.00255>. A 'python' version of this