Expanded model support and improved data return

This release includes support for a number of additional model classes, bringing the complete list to:

• "lm" from stats::lm()
• "glm" from stats::glm(), MASS::glm.nb(), glmx::glmx(), glmx::hetglm()
• "ar" from stats::ar()
• "Arima" from stats::arima()
• "arima0" from stats::arima0()
• "betareg" from betareg::betareg()
• "clm" from ordinal::clm()
• "coxph" from survival::coxph()
• "crch" from crch::crch()
• "gam" from gam::gam()
• "gls" from nlme::gls()
• "hxlr" from crch::hxlr()
• "ivreg" from AER::ivreg()
• "loess" from stats::loess()
• "nls" from stats::nls()
• "nnet" from nnet::nnet(), nnet::multinom()
• "polr" from MASS::polr()
• "rq" from quantreg::rq()
• "selection" from sampleSelection::selection()
• "survreg" from survival::survreg()
• "svm" from e1071::svm()
• "svyglm" from survey::svyglm()

The release also attempts to better standardize the response object returned by prediction(), so that it always includes the original data (being passed at data or retrieved by find_data()). This should make it easier to pass the output of prediction() directly into further data manipulation or plotting functions. find_data() itself is now also generic, making it easier to add model-specific versions.

Various utility functions have also been added:

• seq_range(), generates a sequence of n values within a specified range
• build_datalist() constructs a list of data frames with specified value modifications (an elaboration of expand.grid()
• mean_or_mode() and median_or_mode() provide simply ways of generating a summary statistic from both factor and numeric variables. Uses cases are of the form: lapply(mtcars, mean_or_mode), etc.

The test suite has also been expanded.