person("Remi", "Servien", , "remi.servien@inra.fr", c("aut")),
person("Nathalie", "Villa-Vialaneix", , "nathalie.villa-vialaneix@inra.fr", c("aut", "cre")))
semiparametric framework of SIR. Standard ridge and sparse SIR are

Title: Select Intervals Suited for Functional Regression
Description: Interval fusion and selection procedures for regression with
functional inputs. Methods include a semiparametric approach based
on Sliced Inverse Regression (SIR), as described in
<doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are
also included in the package) and a random forest based approach.
Depends: R (>= 3.5.0), foreach, doParallel
Imports: Matrix, expm, RSpectra, glmnet, Boruta, CORElearn, dplyr,
mixOmics, purrr, ranger, tidyr, tidyselect, adjclust, magrittr,

Title: A Spatial Model of Intra-Tumor Heterogeneity
Maintainer: Phillip B. Nicol <philnicol740@gmail.com>
Description: Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.
Imports: Rcpp (>= 1.0.4), scatterplot3d, stats, graphics, grDevices
Suggests: rgl, knitr, rmarkdown, testthat
Packaged: 2020-06-30 17:35:42 UTC; phillipnicol

Packaged: 2020-07-02 02:21:32 UTC; phillipnicol

Suggests: rgl, igraph, knitr, rmarkdown, testthat
Packaged: 2021-01-04 18:31:46 UTC; phillipnicol

Title: Statistical Inference for Enzyme Kinetics
Author: Donghyun Ra, Kyunghoon Kim, Boseung Choi
Description: Functions for estimating catalytic constant and Michaelis-Menten constant (MM constant) of
stochastic Michaelis-Menten enzyme kinetics model are provided. The likelihood functions
based on stochastic simulation approximation (SSA), diffusion approximation (DA), and
Gaussian processes (GP) are provided to construct posterior functions for the Bayesian
estimation. All functions utilize Markov Chain Monte Carlo (MCMC) methods with Metropolis-
Hastings algorithm with random walk chain and robust adaptive Metropolis-Hastings