(ddPCR) data in R. This is the first non-proprietary software for analyzing
two-channel ddPCR data. An interactive tool was also created and is available

Title: Multi-Data-Driven Sparse PLS Robust to Missing Samples
Description: Allows to build Multi-Data-Driven Sparse PLS models. Multi-blocks with
high-dimensional settings are particularly sensible to this.
Authors@R: person("Hadrien","Lorenzo", role = c("aut", "cre"),

Authors@R: c(person("Hadrien","Lorenzo", role = c("aut", "cre"),

Title: Data-Driven Sparse PLS Robust to Missing Samples for Mono and

Title: Data-Driven Sparse Partial Least Squares Robust to Missing
Samples for Mono and Multi-Block Data Sets
Description: Allows to build Multi-Data-Driven Sparse Partial Least Squares models. Multi-blocks with
high-dimensional settings are particularly sensible to this. It comes with visualization
functions and uses 'Rcpp' functions for fast computations and 'doParallel' to parallelize cross-validation.
This is based on H Lorenzo, J Saracco, R Thiebaut (2019) <arXiv:1901.04380>.
Many applications have been successfully realized.
See <https://hadrienlorenzo.netlify.com/> for more information, documentation and examples.

Author: Przemyslaw Biecek, Teresa Ledwina
Description: Support for adaptive testing
Depends: R (>= 2.1.0), orthopolynom, evd
Packaged: Fri Aug  8 16:31:42 2008; przemek

Author: Przemyslaw Biecek (R code), Teresa Ledwina (support, descriptions)
Description: Support for data driven testing
Packaged: Sun Sep 28 02:33:02 2008; przemek

Author: Przemyslaw Biecek (R code), Teresa Ledwina (support,
Depends: R (>= 2.7.0), orthopolynom, evd

Description: Smooth testing of goodness of fit. These tests are data
driven (alternative hypothesis is dynamically selected based on data). In this
package you will find various tests for exponent, Gaussian, Gumbel and uniform

Title: Bayesian Inference for Differential Equations
person("Philipp H", "Boersch-Supan", email = "pboesu@gmail.com", role = c("aut","cre")),
person("Leah R", "Johnson", email = "lrjohnson0@gmail.com", role = "aut"),
person("Sadie J", "Ryan", email = "datamonkey@gmail.com", role = "aut"))
Description: A Bayesian framework for parameter inference in differential equations.
This approach offers a rigorous methodology for parameter inference as well as
modeling the link between unobservable model states and parameters, and
observable quantities. Provides templates for the DE model, the
observation model and data likelihood, and the model parameters and their prior
distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
to estimate the posterior distributions of the parameters and any derived
quantities, including the model trajectories. Further functionality is provided
to facilitate MCMC diagnostics and the visualisation of the posterior distributions
Imports: truncdist, coda, RColorBrewer, MASS, stats, mvtnorm, graphics,
Suggests: testthat, knitr, rmarkdown, devtools, R.rsp, microbenchmark,
Author: Philipp H Boersch-Supan [aut, cre],
Maintainer: Philipp H Boersch-Supan <pboesu@gmail.com>