boot, Rsolnp, nortest, numDeriv, testthat, moments,

lamW (>= 1.3.0), stats, graphics, grDevices, RColorBrewer,
reshape2, Rcpp (>= 1.0.4), methods
boot, Rsolnp, nortest, numDeriv, testthat, data.table,
moments, knitr, vars,

Lambert W x F distributions are a generalized framework to analyze
skewed, heavy-tailed data. It is based on an input/output system, where the
output random variable (RV) Y is a non-linearly transformed version of an input
RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
The transformed RV Y has a Lambert W x F distribution. This package contains
functions to model and analyze skewed, heavy-tailed data the Lambert Way:
simulate random samples, estimate parameters, compute quantiles, and plot/
print results nicely. The most useful function is 'Gaussianize',
which works similarly to 'scale', but actually makes the data Gaussian.
A do-it-yourself toolkit allows users to define their own Lambert W x
'MyFavoriteDistribution' and use it in their analysis right away.
boot, Rsolnp, nortest, numDeriv, testthat, data.table,
moments, knitr, markdown, vars,

c(
person(given = "Isobel",
family = "Barrott",
role = c("aut", "cre"),
email = "isobel.barrott@gmail.com"),
person(given = "Jessica",
family = "Barrett",
role = c("aut"),
email = "jessica.barrett@mrc-bsu.cam.ac.uk"),
person(given = "Ruth",
family = "Keogh",
role = c("ctb")),
person(given = "Michael",
family = "Sweeting",
role = c("ctb")),
person(given = "David",
family = "Stevens",
role = c("ctb")))
The landmark approach allows survival predictions to be
updated dynamically as new measurements from an individual are recorded.
The idea is to set predefined time points, known as "landmark times",
and form a model at each landmark time using only the individuals in the
risk set. This package allows the longitudinal data to be modelled
either using the last observation carried forward or linear mixed
effects modelling. There is also the option to model competing risks,
either through cause-specific Cox regression or Fine-Gray regression.
To find out more about the methods in this package, please see
<https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.
nlme, riskRegression, dplyr, pec, methods, prodlim, stats,
survival, mstate, ggplot2,
testthat (>= 3.0.0), knitr, rmarkdown, JM
Isobel Barrott [aut, cre],
Jessica Barrett [aut],
Ruth Keogh [ctb],
Michael Sweeting [ctb],
David Stevens [ctb]