Connect R with MOA (Massive Online Analysis -
    <https://moa.cms.waikato.ac.nz/>) to build classification models and
    regression models on streaming data or out-of-RAM data.
    Also streaming recommendation models are made available.

External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    <https://moa.cms.waikato.ac.nz/>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.

Supports automated Markov chain Monte Carlo for arbitrarily structured correlation
    matrices. The user supplies data, a correlation matrix in symbolic form, the current state
    of the chain, a function that computes the log likelihood, and a list of prior distributions.
    The package's flagship function then carries out a parameter-at-a-time update of all correlation
    parameters, and returns the new state. The method is presented in Hughes (2023), in preparation.

Graphical visualization of the birds' molt to facilitate the creation of molting graph for passerines having 9 (Rmolt(data,9)) or 10 primaries (Rmolt(data,10)), and also only for the 10 first primaries (Rmolt(data,"10_0")).

Functions to support rigorous retrospective data harmonization 
    processing, evaluation, and documentation across datasets from different 
    studies based on Maelstrom Research guidelines. The package includes the 
    core functions to evaluate and format the main inputs that define the 
    harmonization process, apply specified processing rules to generate 
    harmonized data, diagnose processing errors, and summarize and evaluate 
    harmonized outputs. The main inputs that define the processing are a 
    DataSchema (list and definitions of harmonized variables to be generated) 
    and Data Processing Elements (processing rules to be applied to generate 
    harmonized variables from study-specific variables). The main outputs of 
    processing are harmonized datasets, associated metadata, and tabular and 
    visual summary reports. As described in 
    Maelstrom Research guidelines for rigorous retrospective data 
    harmonization (Fortier I and al. (2017) <doi:10.1093/ije/dyw075>).