High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). 
              Currently supports frailty data analysis. Allows for Weibull and 
              Exponential distribution. Includes function for interval censored data.

Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication.

Provides tools for optimizing sequencing resource allocation
    and estimating pathogen lineage prevalence under real-world genomic
    surveillance conditions. Implements constrained allocation optimization
    for limited sequencing capacity across multiple regions and sample
    sources. Includes Horvitz-Thompson and post-stratified estimators that
    account for unequal sequencing rates, delay-adjusted nowcasting for
    right-censored reporting data, and combined design-weighted
    delay-corrected inference with uncertainty propagation.

Instrumental variable analysis methods for causal inference on survival data based on the Cox model allowing for various treatment and effect types, including orthogonality method-of-moments instrumental variable estimation for the Cox model, two-stage residual inclusion Cox estimation with frailty, sequential trial emulation, sequential Cox analyses, and sequential two-stage residual inclusion Cox analyses. Methodological background includes MacKenzie et al. (2014) <doi:10.1007/s10742-014-0117-x>, Martinez-Camblor et al. (2019) <doi:10.1093/biostatistics/kxx062>, Martinez-Camblor et al. (2019) <doi:10.1111/rssc.12341>, Gran et al. (2010) <doi:10.1002/sim.4048>, and Keogh et al. (2023) <doi:10.1002/sim.9718>.

Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.