Estimation of models with dependent variable left-censored at zero.
             Null values may be caused by a selection process
	     Cragg (1971) <doi:10.2307/1909582>, insufficient resources
	     Tobin (1958) <doi:10.2307/1907382>, or infrequency of purchase
	     Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.

The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.

A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <doi:10.1371/journal.pcbi.1010420>. It is dedicated to dealing with multiple imputation for proteomics.

Tools for training, selecting, and evaluating maximum entropy
    (and standard logistic regression) distribution models. This package 
    provides tools for user-controlled transformation of explanatory variables, 
    selection of variables by nested model comparison, and flexible model 
    evaluation and projection. It follows principles based on the maximum-
    likelihood interpretation of maximum entropy modeling, and uses infinitely-
    weighted logistic regression for model fitting. The package is described in 
    Vollering et al. (2019; <doi:10.1002/ece3.5654>).

Implements methods to estimate conditional outcome means in 
    settings with missingness-not-at-random and incomplete auxiliary variables. 
    Specifically, this package implements the marginalization over incomplete
    auxiliaries (MIA) method. 
    The package supports continuous and binary outcomes, and 
    supports auxiliary variables that are normal, binary, and categorical.

Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data.
    Validate the results of an MIC experiment by comparing observed MIC values to
    a gold standard assay, in line with standards from the International Organization for
    Standardization (2021) <https://www.iso.org/standard/79377.html>.