Allows you to read and change the state of 'LIFX' smart light bulbs via the 'LIFX' developer api <https://api.developer.lifx.com/>.
    Covers most 'LIFX' api endpoints, including changing light color and brightness, selecting lights by id, group or location as well as activating effects.

Tree based algorithms can be improved by introducing boosting frameworks.
    'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.
    This package offers an R interface to work with it.
    It is designed to be distributed and efficient with the following advantages:
        1. Faster training speed and higher efficiency.
        2. Lower memory usage.
        3. Better accuracy.
        4. Parallel learning supported.
        5. Capable of handling large-scale data.
    In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions.
    Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.

Import, processing, validation, and visualization of personal light exposure measurement data from wearable devices. The package implements features such as the import of data and metadata files, conversion of common file formats, validation of light logging data, verification of crucial metadata, calculation of common parameters, and semi-automated analysis and visualization.

The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
   server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
   by itself cannot and will not create any visualizations.

Split your 'rmarkdown' or 'quarto' files by sections into a
    tibble: titles, text, chunks. Rebuild the file from the tibble.