Description: 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, segmentation/change-point detection 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, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology 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.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].
Packaged: 2022-05-12 13:30:53 UTC; Valentinian

Title: B Cell Receptor Somatik Hyper Mutation Analysis
Description: B Cell Receptor Somatik Hyper Mutation Analysis provide analysis
of nucleotide frequency, transition, transversion, reconstruction of germline
sequences based on IMGT outputs, calculate and plot the percentage difference
at 6 positions to a nucleotide mutation to identify the hotspot motifs and
calculate and plot average mutation frequency of nucleotide mutations resulting
Depends: R (>= 3.2.5), ggplot2, reshape2, grid

Title: B Cell Receptor Somatic Hyper Mutation Analysis
Description: Includes a set of functions to analyze for instance nucleotide frequencies as well as transition and  transversion. Can reconstruct germline sequences based on the international ImMunoGeneTics information system (IMGT/HighV-QUEST) outputs, calculate and plot the difference (%) of nucleotides at 6 positions around a mutation to identify and characterize hotspot motifs as well as calculate and plot average mutation frequencies of nucleotide mutations resulting in amino acid substitution.
Suggests: graphics, utils, knitr, rmarkdown