R (>= 2.0.0), boot, geepack, MASS, nlme, nnet, survival

Inverse Probability Weighted Cox Model with Corrected Sandwich
Variance
Di Shu <shudi1991@gmail.com>, Rui Wang <rwang@hsph.harvard.edu>
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.

Inverse Probability Weighting Estimation of Average Treatment
Effect with Outcome Misclassification
Di Shu <shudi1991@gmail.com>, Grace Y. Yi <yyi@uwaterloo.ca>
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighting (IPW) estimation of average treatment effect (ATE) with misclassified 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.

Inverse Probability Weighted Estimation of Average Treatment
Effect with Misclassified Binary Outcome
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.

Protein Quantification in Mass Spectrometry-Based Proteomics
c(person("Thang", "Pham", email = "t.pham@amsterdamumc.nl",
role = c("aut", "cre", "cph", "ctb"),
comment = c(ORCID = "0000-0003-0333-2492")),
person("Alex", "Henneman", email = "a.henneman@amsterdamumc.nl",
role = c("ctb"),
comment = c(ORCID = "0000-0002-3746-4410")))
An implementation of the maximal
peptide ratio extraction module of the MaxLFQ algorithm by
Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a complete
pipeline for processing proteomics data in data-independent acquisition mode.
It offers additional options for protein quantification using the N most intense
fragment ions, using all
fragment ions, and a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160).