Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

It offers comprehensive tools for the analysis of functional
    time series data, focusing on white noise hypothesis testing and
    goodness-of-fit evaluations, alongside functions for
    simulating data and advanced visualization techniques, such as 3D
    rainbow plots. These methods are described in Kokoszka, Rice, and Shang (2017)  <doi:10.1016/j.jmva.2017.08.004>, 
    Yeh, Rice, and Dubin (2023) <doi:10.1214/23-EJS2112>, Kim, Kokoszka, and Rice (2023) <doi:10.1214/23-ss143>, and 
    Rice, Wirjanto, and Zhao (2020) <doi:10.1111/jtsa.12532>.

Full Consistency Method (FUCOM) for multi-criteria decision-making (MCDM), developed by Dragam Pamucar in 2018 (<doi:10.3390/sym10090393>). The goal of the method is to determine the weights of criteria such that the deviation from full consistency is minimized. Users provide a character vector specifying the ranking of each criterion according to its significance, starting from the criterion expected to have the highest weight to the least significant one. Additionally, users provide a numeric vector specifying the priority values for each criterion. The comparison is made with respect to the first-ranked (most significant) criterion. The function returns the optimized weights for each criterion (summing to 1), the comparative priority (Phi) values, the mathematical transitivity condition (w) value, and the minimum deviation from full consistency (DFC).

Provides utilities to facilitate handling of Fude Polygon data 
    downloadable from the Ministry of Agriculture, Forestry and Fisheries 
    website <https://open.fude.maff.go.jp>.

Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package 'fuel' implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.