Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.

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>).

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>.