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`data` |
data.frame containing all data |

`continList` |
continList Vector of continuous variable names |

`catList` |
catList Vector of categorical variable names |

`grouping` |
grouping Grouping variable to test for equal variance and other multiple group based assumptions |

`minCount` |
Minimum total count |

`minExpectedCountProportion` |
Minimum proportion of cells that have an expected count of 5 or more to still use Chi.square test. Default of 75 \itemmaxLevelsCutoffMaximum number of unique levels to a variable to still analyze. If the variable exceeds this cutoff it will be dropped from the analysis lists. \itemdropOneRowIf TRUE, any variables with only 1 unique level will be dropped from analysis lists. \itemdropNLessThanMinimum number non-NA responses a variable must have before being dropped from the analysis lists. Default of 1 \itemtestNormalityIf TRUE, normality assumptions are checked for continuous variables \itemminDiscreteValuesminDiscreteValues Minimum number of discrete values of a continuous variable to still be tested with parametric methods. \itemvarEqualIf TRUE, and grouping is non-NULL, equal variance assumptions will be tested \itemverboseIf TRUE, messages about decision criteria will be printed to the console. |

List of vectors indicating whether variances should be tested parametrically, non-parametrically or dropped, for continuous and categorical lists separately pickGroupMethod will test common assumptions for parametric and non-parametric methods for 2 + group methods (e.g. t.test, mann-whitney U, chisq, fisher, ANOVA) and will decide which method to use for each variable. #NULL

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