All functions

AARP

Prices for life insurance

Alder

Nitrogen fixing by alder plants

Birth_weight

Birth weights and maternal data

College_grades

Grades at a small college

Crime

Data from the US FBI Uniform Crime Report, 1960

HDD_Minneapolis

Heating degree days in Minneapolis, Minnesota, USA

Houses_for_sale

Houses for sale

Mussels

Metabolism of zebra mussels

NCI60_snippet

Gene expression in cancer cells.

Oil_history

Historical production of crude oil, worldwide 1880-2014

Runners

Performance of runners in a ten-mile race as they age

School_data

Simulated data bearing on school vouchers

Tadpoles

Swimming speed of tadpoles.

Trucking_jobs

Earnings of workers at a trucking company.

Used_Fords

Prices of used Ford automobiles in 2009

collinearity()

Calculate measures of collinearity

construct_fitting_call()

Construct a call for refitting a model from the model itself

data_from_mod()

Extract training data from model

df_counts()

Formula interface to counts

df_props()

Joint and conditional proportions

df_typical()

Find typical levels of explanatory variables in a model/dataset.

explanatory_vars()

Get the names of the explanatory or response variables in a model

formula_from_mod()

Extract the model formula used in specifying the model

mod_cv()

Compare models with k-fold cross validation

mod_effect()

Calculate effect sizes in a model

mod_ensemble()

Create bootstrapped ensembles of a model

mod_error()

Mean square prediction error

mod_eval() mod_output()

Evaluate a model for specified inputs

mod_eval_fun()

Internal functions for evaluating models

mod_eval_grid()

Create discrete levels of explanatory variables in a model/dataset.

mod_fun()

Transforms a model into a function of inputs -> output

mod_plot()

Plot out model values

mosaicModel

mosaicModel package

reference_values()

Compute sensible values from a data set for use as a baseline