Provides a set of S3 generics to axe components of fitted
    model objects and help reduce the size of model objects saved to disk.

Verification of continually updating time series data where we
    expect new values, but want to ensure previous data remains unchanged. Data
    previously recorded could change for a number of reasons, such as discovery
    of an error in model code, a change in methodology or instrument recalibration.
    Monitoring data sources for these changes is not always possible. Other unnoticed
    changes could include a jump in time or measurement frequency, due to instrument
    failure or software updates. Functionality is provided that can be
    used to check and flag changes to previous data to prevent changes going
    unnoticed, as well as unexpected jumps in time.

Trading of Butterfly Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).

Provides tools to download and extract data from the
    'OzButterflies' Database <doi:10.5281/zenodo.19019034>, which
    contains reflectance spectra, calibrated photographs, and
    cytochrome c oxidase subunit I (COI) sequences of Australian
    butterflies. The main function get_Oz_butterflies() downloads the
    database and extracts selected files to a specified directory using
    user-defined filters.

Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.