Title: Inference for Phase-type Distributions
Description: Functions to perform Bayesian inference on absorption time
data for Phase-type distributions.  Plans to expand this to
include frequentist inference and simulation tools.

person("Iker", "Rivas-González", , "ikerrivas96@gmail.com", role = c("aut", "cre"),
person("Colin", "Guetemme", , "colin.guetemme@live.fr", role = "aut"),
person("Asger", "Hobolth", , "asger@math.au.dk", role = "aut"),
person("Lars Nørvang", "Andersen", , "larsa@math.au.dk", role = "aut")
Description: General implementation of core function from phase-type
theory. 'PhaseTypeR' can be used to model continuous and discrete
phase-type distributions, both univariate and multivariate. The
package includes functions for outputting the mean and (co)variance of
phase-type distributions; their density, probability and quantile
functions; functions for random draws; functions for
reward-transformation; and functions for plotting the distributions as
networks. For more information on these functions please refer to
Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro
Suggests: knitr, partitions, rmarkdown, testthat

person("Lars Nørvang", "Andersen", , "larsa@math.au.dk", role = "aut"),
person("Colin", "Guetemme", , "colin.guetemme@live.fr", role = "aut")

Title: High-Throughput Phenotyping with EHR using a Common Automated
person(family = "PARSE LTD", role =  c("aut", "cre"), email = "software@parse-health.org")
Description: Implement surrogate-assisted feature extraction (SAFE) and
common machine learning approaches to train and validate phenotyping models.
Background and details about the methods can be found at
Imports: graphics, methods, stats, utils, glmnet, RMySQL
Suggests: ggplot2, e1071, randomForestSRC, xgboost, knitr

Suggests: ggplot2, e1071, randomForestSRC, xgboost, knitr, rmarkdown

Title: Unsupervised Gold-Standard Label Free Phenotyping Algorithm for
person("Victor", "Castro", role = "aut"),
person("Clara-Lea", "Bonzel", role = c("aut", "cre"), email = "clbonzel@hsph.harvard.edu"),
person(family = "PARSE LTD", role = "aut", email = "software@parse-health.org")
Description: The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.