Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (2nd
    ed)", Duxbury.

Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (3rd
    ed)", Cengage Learning.

Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.

Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA().

Implements the Bayesian model selection method with suspected latent 
    grouping factor methodology of Metzger and Franck (2020), 
    <doi:10.1080/00401706.2020.1739561>. SLGF detects latent 
    heteroscedasticity or group-based regression effects based on the levels of a 
    user-specified categorical predictor.

Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".

A facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.