Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.

Main function "decode" is used to decode coded key values to plain
    text. Function "code" can be used to code plain text to code if there is a
    1:1 relation between the two. The concept relies on 'keyvalue' objects used
    for translation. There are several 'keyvalue' objects included in the areas
    of geographical regional codes, administrative health care unit codes,
    diagnosis codes and more. It is also easy to extend the use by arbitrary 
    code sets.

Hybrid model is the most promising forecasting method by combining decomposition and deep learning techniques to improve the accuracy of time series forecasting. Each decomposition technique decomposes a time series into a set of intrinsic mode functions (IMFs), and the obtained IMFs are modelled and forecasted separately using the deep learning models. Finally, the forecasts of all IMFs are combined to provide an ensemble output for the time series. The prediction ability of the developed models are calculated using international monthly price series of maize in terms of evaluation criteria like root mean squared error, mean absolute percentage error and, mean absolute error. For method details see Choudhary, K. et al. (2023). <https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf>.

Predict future values with hybrid combinations of Pattern Sequence based
        Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode
        Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based
        hybrid methods.

Tools to apply Ensemble Empirical Mode 
    Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new 
    algorithm, extricate, that performs EEMD in seconds, a linear interpolation 
    algorithm  using the greatest rational common divisor of depth or time, 
    different algorithms to compute instantaneous amplitude, frequency and  
    ratios of frequencies, and functions to verify and visualise the outputs.
    The functions were developed during the CRASH project (Checking the 
    Reproducibility of Astrochronology in the Hauterivian). When using for 
    publication please cite Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, 
    A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2022, 
    "A decomposition approach to cyclostratigraphic signal processing". 
    Earth-Science Reviews 225 (103894).
    <doi:10.1016/j.earscirev.2021.103894>.