Functions for use in perturbing data prior to use of nonparametric smoothers
and clustering.

Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.
mvtnorm, stringr, numDeriv, stats, Matrix

Cluster Independent Algorithm for Rare Cell Types Identification
Identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space <https://github.com/ScialdoneLab>.
circlize, clustree, ComplexHeatmap, plotly, Seurat (>= 4.0),
testthat, knitr, rmarkdown

Jeffrey Durieux [aut, cre], Tom Wilderjans [aut]
Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.

Meta-Clustering for Single-Cell Data Integration and Evaluation
c(
person("Zhiyuan", "Hu", role = c("aut", "cre"),
email = "zhiyuan.cheryl.hu@gmail.com",
comment = c(ORCID = "0000-0002-1688-6032")
),
person("Christopher", "Yau",
role = "aut",
comment = c(ORCID = "0000-0001-7615-8523")
)
)
A workflow of (a) meta-clustering based on
inter-group similarity measures and (b) a ground-truth-free test metric to
assess the biological correctness of integration in real datasets. See Hu Z,
Ahmed A, Yau C (2021) <doi:10.1101/2021.03.29.437525> for
more details.
limma (>= 3.42.0), edgeR (>= 3.28.0), stats (>= 3.6.2),
foreach (>= 1.4.7), Seurat (>= 3.1.0), utils (>= 3.6.2),
pheatmap (>= 1.0.0), dbscan (>= 1.1-5), kernlab (>= 0.9-29),
doParallel, igraph, parallel, graphics, ggplot2, viridis
knitr, rmarkdown, testthat, statmod (>= 1.2.2), cowplot

Generative models for networks with conditionally independent
dyadic structure
Beau Dabbs, Brian Junker, Mauricio Sadinle, Tracy Sweet, A.C. Thomas
Generative models for networks with conditionally independent dyadic structure