R implementation of Maximum Likelihood Principal Component Analysis
    The main idea of this package is to have an alternative way of PCA for 
    subspace modeling that considers measurement errors.
    More details can be found in Peter D. Wentzell (2009) 
    <doi:10.1016/B978-0-444-64165-6.03029-9>.

The RMM  fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.

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.

Provides an intuitive and user-friendly interface for working with emojis in 'R'. It allows users to search, insert, and manage emojis by keyword, category, or through an interactive 'shiny'-based drop-down. The package enables integration of emojis into 'R' scripts, 'R Markdown', 'Quarto', 'shiny' apps, and 'ggplot2' plots. Also includes built-in mappings for commit messages, useful for version control. It builds on established emoji libraries and Unicode standards, adding expressiveness and visual cues to documentation, user interfaces, and reports. For more details see 'Emojipedia' (2024) <https://emojipedia.org> and GitHub Emoji Cheat Sheet <https://github.com/ikatyang/emoji-cheat-sheet/tree/master>.