Packaged: 2019-10-04 11:14:11 UTC; ThinkPad_TJ

Title: Sparse Logistic Functional Principal Component Analysis
Description: 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().
Imports: fda, fdapace, psych, stats, utils

Imports: fda, fdapace, psych, splines, stats

Title: Maximin-Distance (Sliced) Latin Hypercube Designs
Description: 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 the quantitative and the qualitative factors. Details
of the algorithm can be found in Ba, S. (2013), "Optimal Sliced
Latin Hypercube Designs", Technometrics. Important function in

Latin Hypercube Designs", revised for Technometrics. Important
function in this package is "maximinSLHD".

Description: 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".

Title: Selective Locally Linear Inference of Cellular Expression
Description: Provides an implementation of SLICER, an algorithm for inferring cellular trajectories from single cell RNA sequencing data. See Welch, JD, Hartemink AJ, Prins JF (2016) <doi:10.1186/s13059-016-0975-3>.
Imports: alphahull, igraph, lle, grDevices, graphics, stats

Title: Single Cell Linkage by Distance Estimation is SLIDE
Authors@R: c(person("Gourab", "Mukherjee", email = "gourab@usc.edu", role = c("aut")), person("Arjun", "Panda", email = "arjunpanda@gwu.edu", role = c("aut","cre")), person("Ann", "Arvin", email = "aarvin@stanford.edu", role = c("aut")), person("Adrish", "Sen", email = "adrishs@stanford.edu", role = c("aut")), person("Nandini", "Sen", email = "nandinis@stanford.edu", role = c("aut")))
Description: This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells
based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the
two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.