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
bsint                   Construct the bias terms for going from model
                        layer to layer to carry forward an offset to
                        mimic a linear model
calceloss               calculate cross-entry for multinomial outcomes
cox.sat.dev             Calculate the CoxPH saturated log-likelihood
cv.glmnetr              Get a cross validation informed relaxed lasso
                        model fit.
cv.stepreg              Cross validation informed stepwise regression
                        model fit.
diff_time               Output to console the elapsed and split times
diff_time1              Get elapsed time in c(hour, minute, secs)
dtstndrz                Standardize a data set
factor.foldid           Generate foldid's by factor levels
getlamgam               get numerical values for lam and gam
glmnetr                 Fit relaxed part of lasso model
glmnetr.compcv          Compare cross validation fits from a
                        nested.glmnetr output.
glmnetr.compcv0         A glmnetr specifc paired t-test
glmnetr.foldid          Set up random folds stratified by a 0, 1
                        indicator
glmnetr.simdata         Generate example data
glmnetr_devratio        Get Deviance ratio.
glmnetrll_1fold         Evaluate fit of leave out fold
nested.glmnetr          Using nested cross validation, describe and
                        compare fits of various cross validation
                        informed machine learning models.
plot.cv.glmnetr         Plot cross-validation deviances, or model
                        coefficients.
plot.glmnetr            Plot the relaxed lasso coefficients.
plot.nested.glmnetr     Plot the cross validated relaxed lasso
                        deviances or coefficients from a nested.glmnetr
                        call.  See plot.cv.glmnetr().
predict.cv.glmnetr      Give predicteds based upon a cv.glmnetr()
                        output object.
predict.cv.stepreg      Beta's or predicteds based upon a cv.stepreg()
                        output object.
predict.glmnetr         Get predicteds or coefficients using a glmnetr
                        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()
prednn_tl               predicted values from an ann_tab_cv output
                        object based upon the model and its lasso model
                        used for generating an offset
preds_1                 Get predictors form a stepwise regression
                        model.
print.nested.glmnetr    Print an abbreviated summary of a
                        nested.glmnetr() output object
stepreg                 Fit the steps of a stepwise regression.
summary.cv.glmnetr      Output summary of a cv.glmnetr() output object.
summary.cv.stepreg      Summarize results from a cv.stepreg() output
                        object.
summary.nested.glmnetr
                        Summarize a nested.glmnetr() output object
summary.stepreg         Briefly summarize steps in a stepreg() output
                        object, i.e. a stepwise regression fit
wtlast                  Construct the weights for going from the last
                        hidden layer to the last layer of the model,
                        not counting any activation, to carry forward
                        an offset to mimic a linear model
wtmiddle                Construct the weights for going between two
                        hidden layers, carrying forward an offset term
                        to mimic a linear model
wtzero                  Construct the weights for going from the
                        observed data with an offset in column 1 to the
                        first hidden layer
xgb.simple              Get a simple XGBoost model fit (no tuning)
xgb.tuned               Get a tuned XGBoost model fit
