The goal is to improve a permutation-based approach (R package: 'pARI') for simultaneous inference on the true discovery proportion by a branch-and-bound algorithm. It is designed to return a list with a bracketing for the true discovery proportion, rather than a single lower bound from 'pARI'. For more details see Andreella. A (2023) <doi:10.1002/sim.9725>.

Pull data from the 'Impect' Customer API <https://glossary.impect.com/api-design>. The package can retrieve data such as events or match sums.

Computes the key metrics for assessing the performance of a liquidity provider (LP) position in a weighted multi-asset Automated Market Maker (AMM) pool. Calculates the nominal and percentage impermanent loss (IL) by comparing the portfolio value inside the pool (based on the weighted geometric mean of price ratios) against the value of simply holding the assets outside the pool (based on the weighted arithmetic mean). The primary function, `impermanent_loss()`, incorporates the effect of earned trading fees to provide the LP's net profit and loss relative to a holding strategy, using a methodology derived from Tiruviluamala, N., Port, A., and Lewis, E. (2022) <doi:10.48550/arXiv.2203.11352>.

Imputing blockwise missing data by imprecise imputation,
    featuring a domain-based, variable-wise, and case-wise strategy. 
    Furthermore, the estimation of lower and upper bounds for 
    unconditional and conditional probabilities based on the obtained
    imprecise data is implemented.
    Additionally, two utility functions are supplied: one to check 
    whether variables in a data set contain set-valued observations;
    and another to merge two already imprecisely imputed data. 
    The method is described in a technical report by Endres, Fink and
    Augustin (2018, <doi:10.5282/ubm/epub.42423>).

Support for implicit expansion of arrays in operations involving
    arrays of mismatching sizes. This pattern is known as "broadcasting" in
    'Python' and  "implicit expansion" in 'Matlab' and is explained for example in
    the article "Array programming with NumPy" by C. R. Harris et al. (2020)
    <doi:10.1038/s41586-020-2649-2>.