The goal of 'snpsettest' is to provide simple tools that perform
             set-based association tests (e.g., gene-based association tests)
             using GWAS (genome-wide association study) summary statistics. A
             set-based association test in this package is based on the
             statistical model described in VEGAS (versatile gene-based
             association study), which combines the effects of a set of SNPs
             accounting for linkage disequilibrium between markers. This package
             uses a different approach from the original VEGAS implementation to
             compute set-level p values more efficiently, as described in
             <https://github.com/HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest>.

Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.

The methods discussed in this package are new non-parametric methods
    based on sequential normal scores 'SNS' (Conover et al (2017)
    <doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
    usually time series data, which may occur singly or in batches,
    and may be univariate or multivariate. These methods are designed
    to detect changes in the process, which may occur as changes in location
    (mean or median), changes in scale (standard deviation, or variance), or
    other changes of interest in the distribution of the observations,
    over the time observed. They usually apply to large data sets,
    so computations need to be simple enough to be done in a reasonable
    time on a computer, and easily updated as each new observation
    (or batch of observations) becomes available. Some examples and more detail
    in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.