Life and Fertility Tables are appropriate to study the dynamics of 
    arthropods populations. This package provides utilities for constructing
    Life Tables and Fertility Tables, related demographic parameters, and some
    simple graphs of interest. It also offers functions to transform the
    obtained data into a known format for better manipulation. In addition,
    two methods for obtaining the confidence interval are included.

Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy of Knight et al (2017) <doi:10.1007/s11222-016-9698-2>.

Persistent reproducible reporting by containerization of R Markdown documents.

A bootstrap proportion test for Brand Lift Testing to quantify the effectiveness of online advertising. Methods of the bootstrap proportion test are presented in
    Liu, Yu, Mao, Wu, Dyer (2023) <doi:10.1145/3583780.3615021>.

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.

Fast calculation of Area Under Curve (AUC) metric of a
             Receiver Operating Characteristic (ROC) curve, using the algorithm
             of Fawcett (2006) <doi:10.1016/j.patrec.2005.10.010>. Therefore it
             is appropriate for large-scale AUC metric calculations.

A system for accurately designing complex light regimes using LEDs.
    Takes calibration data and user-defined target irradiances and it tells  you
    what intensities to use.
    For more details see Vong et al. (2025) <doi:10.1101/2025.06.06.658293>.

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.