c(person("Christophe", "Ambroise", role="aut"),
person("Shubham", "Chaturvedi", role="aut"),
person("Alia", "Dehman", role="aut"),
person("Pierre", "Neuvial", role=c("aut", "cre"),
email="pierre.neuvial@math.univ-toulouse.fr"),
person("Guillem", "Rigaill", role="aut"),
person("Nathalie", "Vialaneix", role="aut"),
person("Gabriel", "Hoffman", role="aut"))
stats, graphics, grDevices, Rcpp (>= 1.0.6), Matrix,
sparseMatrixStats, methods, utils, capushe
Christophe Ambroise [aut],
Shubham Chaturvedi [aut],
Alia Dehman [aut],
Pierre Neuvial [aut, cre],
Guillem Rigaill [aut],
Nathalie Vialaneix [aut],
Gabriel Hoffman [aut]

Implements a constrained version of hierarchical agglomerative
clustering, in which each observation is associated to a position, and only
adjacent clusters can be merged. Typical application fields in
bioinformatics include Genome-Wide Association Studies or Hi-C data
analysis, where the similarity between items is a decreasing function of
their genomic distance. Taking advantage of this feature, the implemented
algorithm is time and memory efficient. This algorithm is described in
Ambroise et al (2019)
<https://almob.biomedcentral.com/articles/10.1186/s13015-019-0157-4>.

Confounder-Adjusted Survival Curves and Cumulative Incidence
Functions
person("Robin", "Denz", , "robin.denz@rub.de", role = c("aut", "cre"))
Estimate and plot confounder-adjusted survival curves using
either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values',
various forms of 'Inverse Probability of Treatment Weighting', two
forms of 'Augmented Inverse Probability of Treatment Weighting',
'Empirical Likelihood Estimation' and 'Targeted Maximum Likelihood
Estimation'. Also includes a significance test for the difference
between two adjusted survival curves and the calculation of adjusted
restricted mean survival times.  Additionally enables the user to
estimate and plot cause-specific confounder-adjusted cumulative
incidence functions in the competing risks setting using the same
methods (with some exceptions).
For details, see Denz et. al (2022) <arXiv:2203.10002v1>.
R.utils, doParallel, doRNG, dplyr (>= 1.0.0), foreach, rlang