Implementations of algorithms from
Learning Sparse Penalties for Change-point Detection
using Max Margin Interval Regression, by
Hocking, Rigaill, Vert, Bach
<http://proceedings.mlr.press/v28/hocking13.html>
published in proceedings of ICML2013.
Segmentor3IsBack, neuroblastoma, microbenchmark, cghseg,
testthat, future, directlabels (>= 2017.03.31)

Segmentor3IsBack, neuroblastoma, microbenchmark, testthat,
future, directlabels (>= 2017.03.31)

Segmentor3IsBack, neuroblastoma, microbenchmark, testthat,
future, future.apply, directlabels (>= 2017.03.31)

data.table (>= 1.9.8), geometry, ggplot2

neuroblastoma, jointseg, testthat, future, future.apply,
directlabels (>= 2017.03.31)

c(person(given = "Mirko",
family = "Signorelli",
role = c("aut", "cre", "cph"),
email = "msignorelli.rpackages@gmail.com",
comment = c(ORCID = "0000-0002-8102-3356")),
person(given = "Pietro",
family = "Spitali",
role = "ctb",
email = "p.spitali@lumc.nl"),
person(given = "Roula",
family = "Tsonaka",
role = "ctb",
email = "s.tsonaka@lumc.nl"))
Computes the penalized regression calibration (PRC)
method, that allows to predict survival using high-dimensional
longitudinal predictors. PRC is described in Signorelli et al.  (in
review).
foreach, doParallel, glmnet, nlme, survival, MASS, survcomp,
survivalROC, ptmixed, stats, dplyr
Mirko Signorelli [aut, cre, cph]
(<https://orcid.org/0000-0002-8102-3356>),
Pietro Spitali [ctb],
Roula Tsonaka [ctb]

foreach, doParallel, glmnet, nlme, survival, MASS, stats,
survcomp, survivalROC, dplyr, ptmixed

Computes the penalized regression calibration (PRC)
method, that allows to predict survival using high-dimensional
longitudinal predictors. PRC is described in Signorelli et al.  (in
review, arXiv preprint: <arXiv:2101.04426>)).
foreach, doParallel, glmnet, nlme, survival, MASS, stats,
survcomp, survivalROC, dplyr

Computes penalized regression calibration (PRC), a
statistical method that allows to predict survival from
high-dimensional longitudinal predictors. PRC is described in
Signorelli et al.  (in review, arXiv preprint: <arXiv:2101.04426>)).
foreach, doParallel, glmnet, nlme, survival, MASS, stats,
survcomp, survivalROC, dplyr, Matrix, magic, lcmm