Description: The package allows for the Estimation of Sample-size and Power by Exploring Simulated Study Outcomes.
WE ARE AWARE A BUG ON THE LAST VERSION OF THE TOOL AND I WORKING TO SORT IT OUT.
A NEW VERSION WILL BE AVAILABLE BY THE END OF DECEMBER 2013.
WE ARE ALSO WORKING ON A WEB-BASED INTERFACE WHICH THAT WILL BE AVAILABLE AT www.espresso-research.org

Title: Efficient Sequential Testing with Evidence Ratios
Authors@R: person("Ladislas", "Nalborczyk", email = "ladislas.nalborczyk@gmail.com",
Description: An implementation of sequential testing that uses evidence ratios
computed from the Akaike weights of a set of models. These weights are
being computed using either the Akaike Information Criterion (AIC) or the
Bayesian Information Criterion (BIC), and following
Burnham & Anderson (2004) recommendations.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference:
Understanding AIC and BIC in model selection.
Sociological Methods and Research, 33(2), 261-304.
Imports: lme4, dplyr, magrittr, ggplot2, rlang

computed from the weights of a set of models. These weights correspond either
to Akaike weights computed from the Akaike Information Criterion (AIC) or the
Bayesian Information Criterion (BIC) and following Burnham & Anderson
weights computed from the WAIC or the LOO-IC of models fitted
with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).
Imports: brms, lme4, dplyr, magrittr, tidyr, ggplot2, rlang, foreach,
Author: Ladislas Nalborczyk [aut, cre] (0000-0002-7419-9855)

Title: Experiment-Selector CV-TMLE for Integration of Observational and
person("Lauren Eyler", "Dang", email = "lauren.eyler@berkeley.edu", role = c("cre","aut")),
person("Maya", "Petersen", email = "mayaliv@berkeley.edu", role = "aut"),
person("Mark", "van der Laan", email = "laan@berkeley.edu", role = "aut"))
Maintainer: Lauren Eyler Dang <lauren.eyler@berkeley.edu>
Description: The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) <arXiv:2210.05802>.
Depends: R (>= 4.2), SuperLearner (>= 2.0.28)
Packaged: 2022-11-01 18:46:48 UTC; laureneeyler