A set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz").

Simulates stochastic hybrid models for transmission of infectious
    diseases in dynamic networks. It is a metapopulation model in which each
    node in the network is a sub-population and disease spreads within nodes
    and among them, combining two approaches: stochastic simulation algorithm
    (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based
    approach, respectively. Equations that models spread within nodes are
    customizable and there are two link types among nodes: migration and
    influence (commuting). More information in Fernando S. Marques,
    Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.

Using hybrid data, this package created a vividly colored hybrid heat map.
    The input is two files which are auto-selected.
    The first file has three columns, the first two for pairs of species,
    with the third column for the hybrid experiment code (an integer).
    The second file is a list of code and their descriptions in two 
    columns. The output is a figure showing the hybrid heat map with a color legend.

Method and tool for generating hybrid time series forecasts using
        an error remodeling approach. These forecasting approaches utilize a recursive 
        technique for modeling the linearity of the series using a linear method 
        (e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster
        using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps: 
        firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step, 
        and finally, the forecasts from both the steps are combined to produce the final output. This method additionally 
        provides the confidence intervals as needed. Ten different models can be implemented using this package.
        This package generates different types of hybrid error correction models for time series forecasting 
        based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020), 
        Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022)
        <doi:10.1016/S0925-2312(01)00702-0> <doi:10.1016/j.physa.2019.121266> 
        <doi:10.1016/j.chaos.2020.109850> <doi:10.1109/IJCNN52387.2021.9533747> 
        <doi:10.1007/978-3-030-72834-2_29> <doi:10.1007/s11071-021-07099-3>.