Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or  modelled  using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.

An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.

An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighted (IPW) estimation of average treatment effect (ATE) with misclassified binary outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals.

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.