Description: Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the 	examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.

Title: Advanced Gene Set Enrichment Analysis
Description: Implements an algorithm to conduct advanced
gene set enrichment analysis on the results of genomics experiments.

Title: Group Testing Procedures for Signal Detection and
Description: It provides cumulative distribution function (CDF),
quantile, p-value, statistical power calculator and random number generator
for a collection of group-testing procedures, including the Higher Criticism
tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests,
the one-sided phi-divergence tests, etc. The input are a group of p-values.
The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of
signal detection, the null hypothesis means no signals. In the context of the
goodness-of-fit testing, which contrasts a group of i.i.d. random variables to
a given continuous distribution, the input p-values can be obtained by the CDF
transformation. The null hypothesis means that these random variables follow the
given distribution. For reference, see Hong Zhang, Jiashun Jin and Zheyang Wu. "Distributions and
Statistical Power of Optimal Signal Detection Methods in Finite Samples",

given distribution. For reference, see Hong Zhang, Jiashun Jin and Zheyang Wu.
"Distributions and Statistical Power of Optimal Signal-Detection Methods In Finite Cases",

Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Butler A and Satija R (2017) <doi:10.1101/164889> for more details.
person(given = 'Rahul', family = 'Satija', email = 'rsatija@nygenome.org', role = 'aut'),
person(given = 'Andrew', family = 'Butler', email = 'abutler@nygenome.org', role = 'aut'),
person(given = 'Paul', family = 'Hoffman', email = 'seuratpackage@gmail.com', role = c('aut', 'cre')),
person(given = 'Jeff', family = 'Farrell', email = 'jfarrell@g.harvard.edu', role = 'ctb'),
person(given = 'Shiwei', family = 'Zheng', email = 'szheng@nygenome.org', role = 'ctb'),
person(given = 'Christoph', family = 'Hafemeister', email = 'chafemeister@nygenome.org', role = 'ctb'),
person(given = 'Patrick', family = 'Roelli', email = 'proelli@nygenome.org', role = 'ctb')
Depends: R (>= 3.2.0), ggplot2, cowplot, Matrix,
Imports: methods, ROCR, stringr, mixtools, lars, ica, tsne, Rtsne, fpc,
ape, VGAM, pbapply, igraph, FNN, caret, e1071, dplyr,
RColorBrewer, MASS, irlba, reshape2, gridExtra, gplots, gdata,
Rcpp, tclust, ranger, compositions, NMF, NMOF, dtw, SDMTools,
plotly, diffusionMap, Hmisc, tidyr, ggjoy