John Michael O Ranola, Kenneth Lange, Steve Horvath, Peter
Langfelder

Propensity score methods for assigning treatment in randomized
trials
This package contains functions to run propensity-biased
allocation to balance covariate distributions in sequential
trials.  Currently this package only supports trials comparing
two groups.

Propensity Score Methods for Assigning Treatment in Randomized
Trials
Contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently only supports trials comparing two groups.

This package contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently this package only supports trials comparing two groups.

Treatment Effect Estimate in Strata with Missing Data
c(
person("Ning", "Leng", email = "leng.ning@gene.com", role = "aut"),
person("Heng","Wang", email="wangh107@gene.com", role=c("aut", "cre")),
person("Shengchun","Kong", email="kong.shengchun@gene.com", role="aut"),
person("Dominik Heinzmann", email = "dominik.heinzmann@roche.com", role="aut")
)
Estimate treatment effect in strata when subjects have missing strata labels,
via inverse probability weighting or propensity score matching.
Matching, rlang, plyr, nnet, ggplot2, survminer, dplyr,
gridExtra, gtable, grid, pROC, scales
Ning Leng [aut],
Heng Wang [aut, cre],
Shengchun Kong [aut],
Dominik Heinzmann [aut]

Generates Various Protein Descriptors for Machine Learning
Algorithms
c(
person("Berat", "Kurar", email = "beratkurar@gmail.com", role = c("aut", "cre")),
person("Sami", "Abu Sneineh", email = "sami.taha@ppu.edu", role = "aut"),
person("Yaqoub", "Ashhab", email = "yashhab@ppu.edu", role = "aut"))
An implementation of protein descriptors in R. These descriptors
combine the advantages of being fixed length and including partial sequential
effect: Various length of protein sequences are described with fixed length
vectors that are suitable for machine learning algorithms, and still includes
partial sequential effect.
Berat Kurar [aut, cre],
Sami Abu Sneineh [aut],
Yaqoub Ashhab [aut]