stats, cluster, coda, knitr, crayon, plyr

Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.

Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.
Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>.
Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.

A Bayesian Semiparametric Factor Analysis Model for Subtype
Identification (Clustering)
Jiehuan Sun [aut, cre], Joshua L. Warren [aut], and Hongyu Zhao [aut]
Gene expression profiles are commonly utilized to infer disease
subtypes and many clustering methods can be adopted for this task.
However, existing clustering methods may not perform well when
genes are highly correlated and many uninformative genes are included
for clustering. To deal with these challenges, we develop a novel
clustering method in the Bayesian setting. This method, called BCSub,
adopts an innovative semiparametric Bayesian factor analysis model
to reduce the dimension of the data to a few factor scores for
clustering. Specifically, the factor scores are assumed to follow
the Dirichlet process mixture model in order to induce clustering.

Bayesian Context Trees for Discrete Time Series
Ioannis Papageorgiou, Valentinian Mihai Lungu, Ioannis Kontoyiannis
An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, and for prediction with respect to log-loss and 0-1 loss. All the functions here (except generate_data) are implementations of deterministic algorithms that have linear complexity in the length of the input data. Example data sets from finance, genetics and animal communication are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' <arXiv:2007.14900> [stat.ME], July 2020]. R version 4.0.2 or more recent required. Windows users may need to download and install Rtools separately; see <https://cran.r-project.org/bin/windows/Rtools/>. Mac users may need to ensure R uses C++11 to install the BCT package. Also it may help resolve installation errors to download and install the Xcode developer tools from the App Store.
Rcpp (>= 1.0.5), stringr, igraph, readr, grDevices, graphics