Title: Fit Interpretable Machine Learning Models
Description: Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).

Title: Estimate the Confidence Interval and Interpret Step by Step
Description: Estimate confidence intervals for mean, proportion, mean difference
for unpaired and paired samples and proportion difference. Plot the confidence
intervals. Generate documents explaining the statistical result step by step.
Imports: dplyr, purrr, tidyr, rlang, ggplot2, scales, ggbeeswarm,
patchwork, aplot, rstudioapi, rmarkdown, flextable, officer,
Packaged: 2022-01-28 01:32:50 UTC; cardiomoon

Title: Binary Classifier Interpretation functions: Partial Dependence
Description: Create partial dependence plots for binary (cross-validated)
classification models. Currently only binary classification models
estimated with the packages randomForest and ada are supported.
Packaged: 2014-11-21 06:40:26 UTC; michelballings

Title: Binary Classifier Interpretation Functions
Description: Compute permutation- based performance measures and create
partial dependence plots for binary (cross-validated) classification models.
Currently only binary classification models estimated with the packages
Packaged: 2014-12-19 03:34:25 UTC; michelballings

partial dependence plots for (cross-validated) randomForest and ada models.
Packaged: 2015-03-26 14:05:38 UTC; michelballings