DAPAR, emmeans, foreach, imp4p, impute, limma, mice, stringr
c(
person(given = "Marie", family= "Chion", role = c("aut"), email = "mariechion@protonmail.com", comment = c(ORCID = "0000-0001-8956-8388")),
person(given = "Christine", family= "Carapito", role = c("aut"), email = "ccarapito@unistra.fr", comment = c(ORCID = "0000-0002-0079-319X")),
person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@utt.fr", comment = c(ORCID = "0000-0002-0837-8281")),
person(given = "Gordon", family= "Smyth", role = c("ctb")),
person(given = "Davis", family= "McCarthy", role = c("ctb")),
person(given = "Hélène", family= "Borges", role = c("ctb")),
person(given = "Thomas", family= "Burger", role = c("ctb")),
person(given = "Quentin", family= "Giai-Gianetto", role = c("ctb")),
person(given = "Samuel", family= "Wieczorek", role = c("ctb"))
)
Marie Chion [aut] (<https://orcid.org/0000-0001-8956-8388>),
Christine Carapito [aut] (<https://orcid.org/0000-0002-0079-319X>),
Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>),
Gordon Smyth [ctb],
Davis McCarthy [ctb],
Hélène Borges [ctb],
Thomas Burger [ctb],
Quentin Giai-Gianetto [ctb],
Samuel Wieczorek [ctb]
Marie Chion <marie.chion@protonmail.fr>, Frederic Bertrand
<frederic.bertrand@utt.fr>
A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <arxiv:2108.07086>. It is dedicated to dealing with multiple imputation for proteomics.

emmeans, foreach, imp4p, impute, limma, mice, stringr
Biobase, knitr, R.rsp, markdown, rmarkdown

Biobase, knitr, R.rsp, markdown, prettydoc, rmarkdown

Biobase, knitr, R.rsp, markdown, prettydoc, rmarkdown,
ProteoMM

Promotion Time Cure Model with Mis-Measured Covariates
Aurelie Bertrand, Catherine Legrand, Ingrid Van Keilegom
MASS, nleqslv, survival, compiler, distr
Fits Semiparametric Promotion Time Cure Models, taking into account (using a
corrected score approach or the SIMEX algorithm) or not the measurement error
in the covariates, using a backfitting approach to maximize the likelihood.