In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.

An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.

Bayesian regularized quantile regression utilizing sparse priors to 
    promote exact sparsity leads to efficient Bayesian shrinkage estimation, variable 
    selection and statistical inference. In this package, we have implemented robust
    Bayesian variable selection with spike-and-slab priors under high-dimensional
    linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and  
    Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying
    coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, 
    valid robust Bayesian inferences under both models in the presence of heavy-tailed errors
    can be validated on finite samples. Additional models with spike-and-slab priors include 
    robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) 
    <doi:10.1002/sta4.70078>). The Markov Chain Monte Carlo (MCMC) algorithms
    of the proposed and alternative models are implemented in C++.