aicreg                  Identify model based upon AIC criteria from a
                        stepreg() putput
ann_tab_cv              Fit an Artificial Neural Network model on
                        "tabular" provided as a matrix, optionally
                        allowing for an offset term
ann_tab_cv_best         Fit multiple Artificial Neural Network models
                        on "tabular" provided as a matrix, and keep the
                        best one.
best.preds              Get the best models for the steps of a
                        stepreg() fit
boot.factor.foldid      Generate foldid's by 0/1 factor for bootstrap
                        like samples where unique option between 0 and
                        1
calceloss               calculate cross-entry for multinomial outcomes
calplot                 Construct calibration plots for a
                        nested.glmnetr output object
cox.sat.dev             Calculate the CoxPH saturated log-likelihood
cv.glmnetr              Get a cross validation informed relaxed lasso
                        model fit. Available to the user but intended
                        to be call from nested.glmnetr().
cv.stepreg              Cross validation informed stepwise regression
                        model fit.
devrat_                 Calculate deviance ratios for CV based
diff_time               Output to console the elapsed and split times
diff_time1              Get elapsed time in c(hour, minute, secs)
factor.foldid           Generate foldid's by factor levels
get.foldid              Get foldid's with branching for cox, binomial
                        and gaussian models
get.id.foldid           Get foldid's when id variable is used to
                        identify groups of dependent sampling units.
                        With branching for cox, binomial and gaussian
                        models
glmnetr.cis             A redirect to nested.cis()
glmnetr.compcv          A redirect to nested.compare
glmnetr.simdata         Generate example data
glmnetr_seed            Get seeds to store, facilitating replicable
                        results
nested.cis              Calculate performance measure "nominal" CI's
                        and p's
nested.compare          Compare cross validation fit performances from
                        a nested.glmnetr output.
nested.glmnetr          Using (nested) cross validation, describe and
                        compare some machine learning model
                        performances
orf_tune                Fit a Random Forest model on data provided in
                        matrix and vector formats.
plot.cv.glmnetr         Plot cross-validation deviances, or model
                        coefficients.
plot.glmnetr            Plot the relaxed lasso coefficients.
plot.nested.glmnetr     Plot results from a nested.glmnetr() output
plot_perf_glmnetr       Plot nested cross validation performance
                        summaries
predict.cv.glmnetr      Give predicteds for elastic net models form a
                        nested.glmnetr() output object.
predict.cv.glmnetr.el   Give predicteds for elastic net models form a
                        nested..glmnetr() output object.
predict.cv.glmnetr.list
                        Give predicteds for elastic net models form a
                        nested..glmnetr() output object.
predict.cv.stepreg      Beta's or predicteds based upon a cv.stepreg()
                        output object.
predict.nested.glmnetr
                        Give predicteds based upon the cv.glmnet output
                        object contained in the nested.glmnetr output
                        object.
predict_ann_tab         Get predicteds for an Artificial Neural Network
                        model fit in nested.glmnetr()
print.nested.glmnetr    A redirect to the summary() function for
                        nested.glmnetr() output objects
print.orf_tune          Print output from orf_tune() function
print.rf_tune           Print output from rf_tune() function
rederive_orf            Rederive Oblique Random Forest models not kept
                        in nested.glmnetr() output
rederive_rf             Rederive Random Forest models not kept in
                        nested.glmnetr() output
rederive_xgb            Rederive XGB models not kept in
                        nested.glmnetr() output
rf_tune                 Fit a Random Forest model on data provided in
                        matrix and vector formats.
roundperf               round elements of a summary.glmnetr() output
stepreg                 Fit the steps of a stepwise regression.
summary.cv.glmnetr      Output summary for elastic net models fit
                        within a nested.glmnetr() output object.
summary.cv.stepreg      Summarize results from a cv.stepreg() output
                        object.
summary.nested.glmnetr
                        Summarize a nested.glmnetr() output object
summary.orf_tune        Summarize output from rf_tune() function
summary.rf_tune         Summarize output from rf_tune() function
summary.stepreg         Briefly summarize steps in a stepreg() output
                        object, i.e. a stepwise regression fit
xgb.simple              Get a simple XGBoost model fit (no tuning)
xgb.tuned               Get a tuned XGBoost model fit
