Depends: R (>= 2.3.1), Rcmdr (>= 1.3-0), car, multcomp, leaps, lattice, grid, grDevices

Title: R Commander Miscellaneous Functions
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.

Depends: R (>= 3.0.0), grDevices, utils, car, sandwich

Depends: R (>= 3.4.0), utils, car (>= 3.0-0), sandwich
Imports: abind, colorspace, Hmisc (>= 4.1-0), MASS, e1071, foreign,
haven, readstata13, readxl, graphics, grDevices, stats, nortest

Depends: R (>= 3.0.0), utils, car, sandwich
Imports: abind, colorspace, Hmisc, MASS, e1071, readxl, graphics,

Imports: abind, colorspace, Hmisc, MASS, e1071, foreign, haven,
readstata13, readxl, graphics, grDevices, stats, nortest

Depends: R (>= 3.5.0), utils, car (>= 3.0-0), sandwich

haven, readstata13, readxl, graphics, grDevices, stats,

Title: Rcmdr Plug-In for Business and Customer Analytics
Description: This package provides an Rcmdr "plug-in" to accompany the
book Business and Customer Analytics: A Hands Approach by

Suggests: rgl, foreign, rpart, rpart.plot, nnet

Description: This package provides an Rcmdr "plug-in" to accompany
the book Customer and Business Analytics: Applied Data Mining for
Business Decision Making Using R by Daniel S. Putler and Robert E.

book Customer and Business Analytics: Applied Data Mining for
Business Decision Making Using R by Daniel S. Putler and Robert

Imports: car (>= 2.0-21), RcmdrMisc (>= 1.0-1), nnet, foreign, rpart,
Description: An Rcmdr "plug-in" to accompany the book Customer and
Business Analytics: Applied Data Mining for Business Decision
Making Using R by Daniel S. Putler and Robert E. Krider.

Title: R Commander Plug-in for Case 1 (Object Case) Best-Worst Scaling
Depends: crossdes, support.BWS (>= 0.4-1), support.CEs, survival
Description: Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details on BWS1, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.