Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. 
    However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. 
    By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields.
    The segmentation(), lambda_estimator() functions are the main functions of this package.

Utility functions for mutational signature analysis
             as described in Alexandrov, L. B. (2020)
             <doi:10.1038/s41586-020-1943-3>.
             This package provides two groups of functions. One is
             for dealing with mutational signature "exposures"
             (i.e. the counts of mutations in a sample that are
             due to each mutational signature). The other group of
             functions is for matching or comparing sets of 
             mutational signatures. 'mSigTools' stands for 
             mutational Signature analysis Tools.

Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.

Incorporates a Bayesian monotonic single-index mixed-effect model with a multivariate skew-t likelihood, specifically designed to handle survey weights adjustments. Features include a simulation program and an associated Gibbs sampler for model estimation. The single-index function is constrained to be monotonic increasing, utilizing a customized Gaussian process prior for precise estimation. The model assumes random effects follow a canonical skew-t distribution, while residuals are represented by a multivariate Student-t distribution. Offers robust Bayesian adjustments to integrate survey weight information effectively.