Updated all examples to use the new dataset,
yields.
Topic based vignettes are now available.
Added a new dataset yields that may be useful for
testing purposes.
Fixed issues with knitr causing failing
builds.
Updated docs with newer examples.
fit_models to support model fitting for
several variables for several model types.Major additions
extract_model_info now supports
glmerMod and glmmTMB
get_this now works with numeric input and also
supports data.frame objects.
fit_models extends fit_model by building many models at once.
Other changes
get_stats now drops columns via a vector and not
“non_numeric” as previously.
Metrics from multi_model_1 are now more informative
with the metric and method wrapped in the naming of the result.
df was renamed as old_data in
multi_model_1, newdata to
new_data.
plot_corr now directly accepts
data.frame objects. Arguments like
round_values have also been dropped.
Fixed DOI to Max Kuhn’s paper
Refactored get_mode to be tidy
compliant.
The argument valid was dropped in
multi_model_1.
get_all was dropped in
select_percentile.
select_col, select_percentile,
row_mean_na will be removed in the next release.
row_mean_na is now defunct. Use
na_replace instead.
na_replace no longer allows using functions such as
mean,min, etc. These have been reimplemented
in the package mde
modeleR is now defunct. Use fit_model
instead.
get_this no longer accepts non quoted character
strings.
Better coverage and code tests
Fixes paper citation
New functions
plot_corr has been added to allow plotting of
correlation matrices produced by get_var_corr_.
na_replace_grouped extends na_replace
by allowing replacement of missing values(NAs) by
group.
add_model_predictions allows addition of predicted
values to a data set.
add_model_residuals is an easy to use and
dplyr compatible wrapper that allows addition of residuals
to a data set.
extract_model_info allows easy extraction of common
model attributes such as p values, residuals, coefficients, etc as per
the specific model type. It supports extraction of multiple
attributes.
multi_model_2 allows fitting and predicting in one
function. It is similar to multi_model_1 except it does not
require metrics.
Major Changes
modeleR has been replaced with
fit_model which is an easier to remember name. Usage
remains the same.
fit_model no longer allows direct addition of
predictions. Use add_model_predictions to achieve the
same.
na_replace has been extended to allow for user
defined values.
rowdiff now accepts replacement of the calculation
induced NAs. It does so by using
na_replace.
get_var_corr_ now supports using only a subset of
the data.
Helper functions are no longer exported.
get_data_Stats is now aliased with
get_stats for ease.
get_var_corr no longer has the get_all
argument. Instead, users can provide an option other_vars
vector of subset columns. drop_columns has also been
changed from boolean to a character vector.
Minor bug fixes with respect to the vignette.
Major Changes
Additions
agg_by_group is a new function that manipulates
grouped data. It is fast and robust for many kinds of
functions.
rowdiff is another new function that enable one to
find differences between rows in a data.frame object. `
get_var_corr provides a user-friendly way to find
correlations between data.
get_var_corr_ provides a user-friendly way to find
combination-wise correlations. It is relatively fast depending on how
big one’s data is and/or machine specifications.
get_this is an easy to use helper function to get
metrics,predictions, etc. Currently supports lists and data.frame
objects.
modeleR and row_mean_na were
removed.
Major Modifications
get_data_Stats now supports removal of missing data
as well as using only numeric data.
modeleR has been fixed to handle new data as
expected. It also now supports glm.
multi_model_1 now supports either validation or
working with new data.
row_mean_na has been replaced with na_replace which
is more robust. row_mean_na will be removed in future
versions.