Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).

Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.

Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.

A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.

Implements the compartment model from Tokars (2018) 
  <doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of 
  population-wide impact of vaccination against vaccine-preventable 
  diseases such as influenza.

Functions to read, process and analyse accelerometer
    data related to mechanical loading variables. This package is
    developed and tested for use with raw accelerometer data from
    triaxial 'ActiGraph' <https://theactigraph.com> accelerometers.

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>.