c(
person(given = "Alessandro",
family = "Gasparini",
role = c("aut", "cre", "cph"),
email = "alessandro.gasparini@ki.se",
comment = c(ORCID = "0000-0002-8319-7624")),
person(given = "Ary",
family = "Serpa Neto",
role = "ctb")
)
Alessandro Gasparini [aut, cre, cph]
(<https://orcid.org/0000-0002-8319-7624>),
Ary Serpa Neto [ctb]

c(person(given = "Alessandro",
family = "Gasparini",
role = c("aut", "cre", "cph"),
email = "alessandro.gasparini@ki.se",
comment = c(ORCID = "0000-0002-8319-7624")),
person(given = "Ary",
family = "Serpa Neto",
role = "ctb"))
Produce Kaplan–Meier plots in the style recommended
following the KMunicate study by Morris et al. (2019)
<doi:10.1136/bmjopen-2019-030215>. The KMunicate style consists of
Kaplan-Meier curves with confidence intervals to quantify uncertainty
and an extended risk table (per treatment arm) depicting the number of
study subjects at risk, events, and censored observations over time.
The resulting plots are built using 'ggplot2' and can be further
customised to a certain extent, including themes, fonts, and colour
scales.
broom, covr, devtools, knitr, rmarkdown, simsurv, testthat,
usethis

c(person(given = "Alessandro",
family = "Gasparini",
role = c("aut", "cre", "cph"),
email = "alessandro@ellessenne.xyz",
comment = c(ORCID = "0000-0002-8319-7624")),
person(given = "Ary",
family = "Serpa Neto",
role = "ctb"))

Interactive Document for Working with KNN Analysis
An interactive document on  the topic of K-nearest neighbour (KNN) using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/KNNShiny/>.

A novel strategy for conducting variable selection without prior model topology
knowledge using the knockoff method (Barber and Candes (2015) <doi:10.1214/15-AOS1337>)
with extreme boosted tree models (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>).
This method is inspired by the original knockoff method, where the differences between
original and knockoff variables are used for variable selection with false discovery rate
control. In addition to the original knockoff generating methods, two new sampling methods
are available to be implemented, namely the sparse covariance and principal component
knockoff methods. As results, the indices of selected variables are returned.
glmnet (>= 2.0-18), knockoff, spcov, xgboost, Rdpack (>=
0.11-0), stats, MASS