Implementation of the SigWin-detector for the detection of
regions of increased or decreased gene expression (RIDGEs and
anti-RIDGES) in transcriptome maps and the presentation in so
called RIDGEOGRAMs as described by Marcia A. Inda et al. [BMC
Res Notes 2008; 1:63]

Uses logistic regression to model the probability of
detection as a function of covariates.  This model is then used
with observational survey data to estimate population size,
while accounting for uncertain detection.  See Steinhorst and
Samuel (1989).

Uses logistic regression to model the probability of detection as a function of covariates.
This model is then used with observational survey data to estimate population size, while
accounting for uncertain detection.  See Steinhorst and Samuel (1989).

c(
person("Fieberg", "John",       email = "jfieberg@umn.edu",               role = c("aut")),
person("Schwarz", "Carl James", email = "cschwarz.stat.sfu.ca@gmail.com", role = c("aut","cre")))
formula.tools, Matrix, msm, plyr, stats, survey, utils
car, data.table, ggplot2, kableExtra, knitr, readxl,
reshape2, rmarkdown, R.rsp
Fieberg John [aut],
Schwarz Carl James [aut, cre]

formula.tools, Matrix, msm, plyr, mvtnorm, stats, survey,
utils
car, data.table, GGally, ggplot2, kableExtra, knitr, readxl,
reshape2, rmarkdown, tidyr, tidyverse, R.rsp

c(
person(given = 'Tim', family = 'Stuart', email = 'tstuart@nygenome.org', role = c('aut', 'cre'), comment = c(ORCID = '0000-0002-3044-0897')),
person(given = 'Paul', family = 'Hoffman', email = 'phoffman@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0002-7693-8957')),
person(given = 'Rahul', family = 'Satija', email = 'rsatija@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-9448-8833'))
)
A framework for the analysis and exploration of single-cell chromatin data.
The 'Signac' package contains functions for quantifying single-cell chromatin data,
computing per-cell quality control metrics, dimension reduction
and normalization, visualization, and DNA sequence motif analysis.
Reference: Stuart and Butler et al. (2019) <doi:10.1016/j.cell.2019.05.031>.
BiocGenerics, GenomeInfoDb, GenomicFeatures, GenomicRanges,
IRanges, Matrix, Rsamtools, S4Vectors, Seurat, data.table,
dplyr, future, future.apply, ggplot2, ggseqlogo, irlba,
pbapply, tidyr, zoo, patchwork, Biostrings, BSgenome, gggenes,
grid, stats, utils
'data.R' 'generics.R' 'dimension_reduction.R' 'motifs.R'
'objects.R' 'preprocessing.R' 'utilities.R' 'visualization.R'
testthat (>= 2.1.0), chromVAR, SummarizedExperiment,
TFBSTools, motifmatchr, BSgenome.Hsapiens.UCSC.hg19,
EnsDb.Hsapiens.v75, JASPAR2018