stats, rlang, tidyr, ggplot2, randomForestSRC, Rdpack

Anton Strezhnev, Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto
An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.

Anton Strezhnev, Elissa Berwick, Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto
R (>= 2.0.0), sandwich, lmtest, ggplot2, survey

Soubhik Barari, Elissa Berwick, Jens Hainmueller, Daniel Hopkins, Sean Liu, Anton Strezhnev, Teppei Yamamoto
R (>= 2.0.0), sandwich, lmtest, ggplot2, survey, Matrix
An R implementation of the Average Marginal Component-specific
Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto
T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional
Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.

R (>= 2.10), sandwich, lmtest, ggplot2, survey, Matrix
stats, utils, methods, DT, shiny, shinyjs, shinyBS

person("Chris", "Kennedy", email = "chrisken@gmail.com",
role = c("aut", "cre"))
Toolkit for statistical, machine learning, and targeted learning
analyses. Functionality includes loading & auto-installing packages,
standardizing datasets, creating missingness indicators, imputing missing
values, creating multicore or multinode clusters, automatic SLURM integration,
enhancing SuperLearner and TMLE with automatic parallelization, and many other
SuperLearner analysis & plotting enhancements.
caret, cvAUC, doParallel, doSNOW, foreach, ggplot2, methods,
parallel, pryr, randomForest, RANN, reader, RhpcBLASctl, ROCR,
snow, stringr, SuperLearner, tmle

person(given = "Chris",
family = "Kennedy",
email = "chrisken@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-7444-2766"))
Toolkit for statistical, machine learning, and targeted learning
analyses. Functionality includes loading & auto-installing packages,
standardizing datasets, creating missingness indicators, imputing missing
values, creating multicore or multinode clusters, automatic SLURM
integration, enhancing SuperLearner and TMLE with automatic parallelization,
and many other SuperLearner analysis & plotting enhancements.
caret, checkmate, crayon, cvAUC, doParallel, foreach,
future.apply, ggplot2, h2o, methods, parallel, precrec, pryr,
reader, RhpcBLASctl, ROCR, rpart.plot, stringr, SuperLearner,
tmle, weights
data.table, dplyr, glmnet, lintr, MASS, magrittr, mgcv,
microbenchmark, mlbench, randomForest, RANN, slam, testthat,
xgboost
Chris Kennedy [aut, cre] (<https://orcid.org/0000-0001-7444-2766>)