B Cell Receptor Somatik Hyper Mutation Analysis
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
in amino acid substitution.

B Cell Receptor Somatic Hyper Mutation Analysis
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.

Bias-Corrected Estimates for Generalized Linear Models for
Dependent Data
Nicola Lunardon <nicola.lunardon@unimib.it>, Daniel Scharfstein <dscharf@jhu.edu>
Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.

A Dirichlet Process Mixture Model for Clustering Longitudinal
Gene Expression Data
Jiehuan Sun [aut, cre], Jose D. Herazo-Maya[aut], Naftali Kaminski[aut], Hongyu Zhao [aut], and Joshua L. Warren [aut],
Many clustering methods have been proposed, but
most of them cannot work for longitudinal gene expression data.
'BClustLonG' is a package that allows us to perform clustering analysis for
longitudinal gene expression data. It adopts a linear-mixed effects framework
to model the trajectory of genes over time, while clustering is jointly
conducted based on the regression coefficients obtained from all genes.
To account for the correlations among genes and alleviate the
high dimensionality challenges, factor analysis models are adopted
for the regression coefficients. The Dirichlet process prior distribution
is utilized for the means of the regression coefficients to induce clustering.
This package allows users to specify which variables to use for clustering
(intercepts or slopes or both) and whether a factor analysis model is desired.
More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.