Design of Discrete Choice and Conjoint Analysis
person(given = "Jed",
family = "Stephens",
role = c("aut", "cre"),
email = "STPJED001@myuct.ac.za",
comment = c(ORCID = "0000-0002-0131-703X"))
Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.
stats, far, dplyr, DoE.base, rlist, purrr
Jed Stephens [aut, cre] (<https://orcid.org/0000-0002-0131-703X>)

Explanation of Predictions for Classification and Regression
Models
Package contains methods to generate explanations for individual predictions of
classification and regression models. Weighted averages of individual explanations form explanation of the whole model.
The package extends 'CORElearn' package, but other prediction models can also be explained using a wrapper.
CORElearn (>= 1.47.0),semiArtificial (>= 2.0.0)

Generates explanations for classification and regression models and visualizes them.
Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
in the model. The IME method is slower as it samples from all feature subsets.
For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>,
and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided
and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.
CORElearn (>= 1.50.2),semiArtificial (>= 2.2.5)

CORElearn (>= 1.50.3),semiArtificial (>= 2.2.5)