The 'debar' sequence processing pipeline is designed for denoising high throughput 
    sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package 
    is designed to detect and correct insertion and deletion errors within sequencer outputs. 
    This is accomplished through comparison of input sequences against a profile hidden Markov 
    model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, 
    ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted 
    for through the introduction of a placeholder character. Since the PHMM is a probabilistic 
    representation of the COI barcode, corrections are not always perfect. For this reason 
    'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder 
    characters (default is 7 base pairs in either direction, this feature can be disabled).
    Testing has shown that this censorship results in the correct sequence length being restored, 
    and erroneous base pairs being masked the vast majority of the time (>95%).

Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI.

A general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Efficiency gains of biomarker-guided trials can be quantitatively compared to the traditional all-comers design. For reference, see Zhang et al. (2024) <arXiv:2401.00540>.

Supports propensity score-based methods—including matching, stratification, and weighting—for estimating causal treatment effects. It also implements calibration using negative control outcomes to enhance robustness. 'debiasedTrialEmulation' facilitates effect estimation for both binary and time-to-event outcomes, supporting risk ratio (RR), odds ratio (OR), and hazard ratio (HR) as effect measures. It integrates statistical modeling and visualization tools to assess covariate balance, equipoise, and bias calibration. Additional methods—including approaches to address immortal time bias, information bias, selection bias, and informative censoring—are under development. Users interested in these extended features are encouraged to contact the package authors.