print results nicely. The most useful function is 'Gaussianize',

Description: 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
Imports: nlme, riskRegression, dplyr, pec, methods, prodlim, stats,
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, JM

Title: Langevin Analysis in One and Two Dimensions
Description: Estimate drift and diffusion functions from time series and
generate synthetic time series from given drift and diffusion coefficients.
LinkingTo: Rcpp, RcppArmadillo (>= 0.4.600.0)

role=c('aut','cre')), person('Pedro G.', 'Lind', role='aut'),

Title: Laplace Deconvolution with Noisy Discrete Non-Equally Spaced
Author: Yves Rozenholc (University Paris Descartes), Marianna Pensky (University of Central Florida)
Description: Solves the problem of Laplace deconvolution with noisy discrete non-equally spaced observations on a finite time interval based on expansions of the convolution kernel, the unknown function and the observed signal over Laguerre functions basis. It implements the methodology proposed in the paper "Laplace deconvolution on the basis of time domain data and its application to Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y. Rozenholc in ArXiv (http://arxiv.org/abs/1405.7107).

Author: Yves Rozenholc (University Paris Descartes), Marianna Pensky (University
Description: Solves the problem of Laplace deconvolution with noisy discrete
non-equally spaced observations on a finite time interval based on expansions
of the convolution kernel, the unknown function and the observed signal over
Laguerre functions basis. It implements the methodology proposed in the paper
"Laplace deconvolution on the basis of time domain data and its application to
Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y.