Joint DNA-based disaster victim identification (DVI), as
    described in Vigeland and Egeland (2021)
    <doi:10.21203/rs.3.rs-296414/v1>. Identification is performed by
    optimising the joint likelihood of all victim samples and reference
    individuals. Individual identification probabilities, conditional on
    all available information, are derived from the joint solution in the
    form of posterior pairing probabilities. 'dvir' is part of the
    'pedsuite' collection of packages for pedigree analysis.

Contains functions that do something convenient (e.g. create BMI categories), functions for calculating moving-window statistics efficiently, and functions for generating various figures (e.g. histograms with fitted probability mass/density function).

A set of control charts for batch processes based on the VAR model. The package contains the implementation of T2.var and W.var control charts based on VAR model coefficients using the couple vectors theory. In each time-instant the VAR coefficients are estimated from a historical in-control dataset and a decision rule is made for online classifying of a new batch data. Those charts allow efficient online monitoring since the very first time-instant. The offline version is available too. In order to evaluate the chart's performance, this package contains functions to generate batch data for offline and online monitoring.See in Danilo Marcondes Filho and Marcio Valk (2020) <doi:10.1016/j.ejor.2019.12.038>.

Dual Wavelet based Nonlinear Autoregressive Distributed Lag model has been developed for noisy time series analysis. This package is designed to capture both short-run and long-run relationships in time series data, while incorporating wavelet transformations. The methodology combines the NARDL model with wavelet decomposition to better capture the nonlinear dynamics of the series and exogenous variables. The package is useful for analyzing economic and financial time series data that exhibit both long-term trends and short-term fluctuations. This package has been developed using algorithm of Jammazi et al. <doi:10.1016/j.intfin.2014.11.011>.