Nonparametric survival function estimates and semiparametric
    regression for the multivariate failure time data with right-censoring.
    For nonparametric survival function estimates, the Volterra, Dabrowska,
    and Prentice-Cai estimates for bivariate failure time data may be
    computed as well as the Dabrowska estimate for the trivariate failure
    time data. Bivariate marginal hazard rate regression can be fitted for
    the bivariate failure time data. Functions are also provided to compute
    (bootstrap) confidence intervals and plot the estimates of the bivariate
    survival function. For details, see "The Statistical Analysis of
    Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice,
    R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.

MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python
    tool to predict MHC class I and MHC class II epitopes.
    This package allows one to call MHCnuggets from R.

Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) <doi:10.1080/01621459.2021.2011298>.

Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories.

Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.

An implementation of the multilevel (also known as mixed or random 
    effects) hidden Markov model using Bayesian estimation in R. The multilevel 
    hidden Markov model (HMM) is a generalization of the well-known hidden
    Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The 
    multilevel HMM is tailored to accommodate (intense) longitudinal data of 
    multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. 
    <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow 
    for heterogeneity in the model parameters (transition probability matrix and 
    conditional distribution), while estimating one overall HMM. The model can 
    be fitted on multivariate data with either a categorical, normal, or Poisson 
    distribution, and include individual level covariates (allowing for e.g., 
    group comparisons on model parameters). Parameters are estimated using 
    Bayesian estimation utilizing the forward-backward recursion within a hybrid 
    Metropolis within Gibbs sampler. Missing data (NA) in the dependent 
    variables is accommodated assuming MAR. The package also includes various 
    visualization options, a function to simulate data, and a function to obtain 
    the most likely hidden state sequence for each individual using the Viterbi 
    algorithm.