Title: Simulation Based Inference of Lasso Estimator
Authors@R: c(person("Seunghyun", "Min", email = "seunghyun@ucla.edu", role = c("aut", "cre")), person("Qing", "Zhou", email = "zhou@stat.ucla.edu", role = "aut"))
Description: Estimator augmentation methods for statistical inference on high-dimensional data,
It provides several simulation-based inference methods: (a) Gaussian and
wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
lasso and their de-biased estimators, (b) importance sampler for approximating
p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with
Imports: stats, graphics, msm, mvtnorm, parallel, limSolve, MASS, hdi,
Packaged: 2017-09-15 20:52:00 UTC; seunghyunmin

Title: Estimator Augmentation and Simulation-Based Inference
as described in Zhou, Q. (2014) <arXiv:1401.4425v2>
Packaged: 2017-10-13 19:57:34 UTC; seunghyunmin

Packaged: 2017-11-13 09:49:36 UTC; seunghyunmin

Packaged: 2017-12-01 23:11:51 UTC; seunghyunmin

Authors@R: person("Seunghyun", "Min", email = "seunghyun@ucla.edu", role = c("aut", "cre"))
Description: Simulation based inference of lasso estimator. It provides several
methods to sample lasso estimator: (a) Gaussian and wild multiplier bootstrap for
lasso, group lasso, scaled lasso and scaled group lasso, (b) importance sampler
for lasso, group lasso, scaled lasso and scaled group lasso, (c) Markov chain
Monte Carlo sampler for lasso, (d) post-selection inference for lasso.
See Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309> for details.
Imports: stats, graphics, gglasso, msm, mvtnorm, parallel, limSolve,
Packaged: 2017-09-01 00:20:13 UTC; seunghyunmin

Title: Sample Size Calculation Method for Cost-Effectiveness Studies
Based on Expected Value of Perfect Information
Authors@R: c(person("Sebastien", "Cossin", role=c("aut", "cre"), email="cossin.sebastien@gmail.com"),
Description: We propose a new sample size calculation method for trial-based
cost-effectiveness analyses. Our strategy is based on the value of perfect
information that would remain after the completion of the study.
Collate: 'POP.R' 'INMB_DIRECT.R' 'VAR_INMB_DIRECT.R' 'EVPI.R'
'internal.R' 'INMB.R' 'Lambda.R' 'VAR_INMB.R' 'VAR_INMB_DIFF.R'