Random generation, density function and parameter estimation for the Voigt distribution. The main objective of this package is to provide R users with efficient estimation of Voigt parameters using classic iid data in a Bayesian framework. The estimating function allows flexible prior specification, specification of fixed parameters and several options for Markov Chain Monte Carlo posterior simulation. A basic version of the algorithm is described in: Cannas M. and Piras, N. (2025) <doi:10.1007/978-3-031-96303-2_53>.

'R' implementation of the 'vol2bird' software for generating vertical profiles
    of birds and other biological signals in weather radar data. See Dokter et al.
    (2011) <doi:10.1098/rsif.2010.0116> for a paper describing the methodology.

Trading Strategies for high Option Volatility environment are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).

Generates interactive plots for analysing and visualising 
    three-class high dimensional data. It is particularly suited to visualising 
    differences in continuous attributes such as gene/protein/biomarker 
    expression levels between three groups. Differential gene/biomarker 
    expression analysis between two classes is typically shown as a volcano 
    plot. However, with three groups this type of visualisation is particularly 
    difficult to interpret. This package generates 3D volcano plots and 3-way 
    polar plots for easier interpretation of three-class data.

Interactive adverse event (AE) volcano plot for monitoring clinical trial safety. This tool allows users to view the overall distribution of AEs in a clinical trial using standard (e.g. MedDRA preferred term) or custom (e.g. Gender) categories using a volcano plot similar to proposal by Zink et al. (2013) <doi:10.1177/1740774513485311>. This tool provides a stand-along shiny application and flexible shiny modules allowing this tool to be used as a part of more robust safety monitoring framework like the Shiny app from the 'safetyGraphics' R package.