An implementation of the MaxLFQ algorithm by
    Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive
    pipeline for processing proteomics data in data-independent acquisition mode
    (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>).
    It offers additional options for protein quantification using
    the N most intense fragment ions, using all fragment ions, 
    the median polish algorithm by Tukey (1977, ISBN:0201076160), and a robust linear model.
    In general, the tool can be used to integrate multiple 
    proportional observations into a single quantitative value.

Builds statistical control charts with exact limits for
        univariate and multivariate cases.

Functions to import and handle infrared spectra (import from '.csv' and
  Thermo Galactic's '.spc', baseline correction, binning, clipping,
  interpolating, smoothing, averaging, adding, subtracting, dividing, 
  multiplying, atmospheric correction, 'tidyverse' methods, plotting).

Iterated race is an extension of the Iterated F-race method for
             the automatic configuration of optimization algorithms, that is,
             (offline) tuning their parameters by finding the most appropriate
             settings given a set of instances of an optimization problem.
             M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle,
             and M. Birattari (2016) <doi:10.1016/j.orp.2016.09.002>.

Graphical visualization tools for analyzing the data produced by 'irace'. The 'iraceplot' package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of 'irace' and their performance. The functions just require the log file generated by 'irace' and, in some cases, they can be used with user-provided data.

Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.