c(
person(given = 'Rahul', family = 'Satija', email = 'rsatija@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0001-9448-8833')),
person(given = 'Andrew', family = 'Butler', email = 'abutler@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0003-3608-0463')),
person(given = 'Paul', family = 'Hoffman', email = 'seurat@nygenome.org', role = c('aut', 'cre'), comment = c(ORCID = '0000-0002-7693-8957')),
person(given = 'Tim', family = 'Stuart', email = 'tstuart@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0002-3044-0897')),
person(given = 'Jeff', family = 'Farrell', email = 'jfarrell@g.harvard.edu', role = 'ctb'),
person(given = 'Shiwei', family = 'Zheng', email = 'szheng@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6682-6743')),
person(given = 'Christoph', family = 'Hafemeister', email = 'chafemeister@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6365-8254')),
person(given = 'Patrick', family = 'Roelli', email = 'proelli@nygenome.org', role = 'ctb'),
person(given = "Yuhan", family = "Hao", email = 'yhao@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0002-1810-0822'))
)
grDevices, grid, Matrix (>= 1.3.3), methods, Rcpp (>= 1.0.5),
rlang (>= 0.4.7), stats, tools, utils

future, future.apply, grDevices, grid, Matrix (>= 1.3.3),
methods, progressr, Rcpp (>= 1.0.5), sp, rgeos, rlang (>=
0.4.7), stats, tools, utils
'RcppExports.R' 'zzz.R' 'generics.R' 'graph.R' 'assay.R'
'centroids.R' 'command.R' 'data.R' 'default.R' 'jackstraw.R'
'dimreduc.R' 'segmentation.R' 'keymixin.R' 'molecules.R'
'spatial.R' 'fov.R' 'logmap.R' 'neighbor.R' 'seurat.R'
'utils.R'

future, future.apply, grDevices, grid, Matrix (>= 1.5.0),
methods, progressr, Rcpp (>= 1.0.5), sp (>= 1.5.0), rgeos,
rlang (>= 0.4.7), stats, tools, utils

future, future.apply, grDevices, grid, Matrix (>= 1.5.0),
methods, progressr, Rcpp (>= 1.0.5), rlang (>= 0.4.7), stats,
tools, utils

Change-Point Estimation using Shape-Restricted Splines
In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrapping confidence interval for the change-point is incorporated in the main routine.