Imports: BB, MASS, Matrix, msm, numDeriv, plyr, TMB (>= 1.7.15)

Title: Perform rare variants association analysis based on summation of
Description: This package performs robust nonparametric tests for rare variants
association analysis using summation of partition approaches that incorporate

Title: Score Test Based on Saddlepoint Approximation
Description: Performs score test using saddlepoint approximation to estimate the null distribution.

Title: Score Test and Meta-Analysis Based on Saddlepoint Approximation
Description: Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>.

Title: Simple Progress Bars for Procedural Coding
person('Fabio Ashtar', 'Telarico', email = 'Fabio-Ashtar.Telarico@fdv.uni-lj.si',
Maintainer: Fabio Ashtar Telarico <Fabio-Ashtar.Telarico@fdv.uni-lj.si>
Description: Provides a simple progress bar to use for basic and advanced users that suits all those who prefer procedural programming. It is especially useful for integration into markdown files thanks to the progress bar's customisable appearance.

Title: A New Reduced-Rank Linear Discriminant Analysis Method
Author: Yue S. Niu, Ning Hao, and Bin Dong
Description: A new reduced-rank LDA method which works for high dimensional multi-class data.

Title: Sparse Principal Component Analysis via Random Projections
Author: Milana Gataric, Tengyao Wang and Richard J. Samworth
Description: Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2017) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.

Description: Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.