| 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 |
| glmnetrll_1fold | Evaluate fit of leave out fold |
| glmnetr_devratio | Get Deviance ratio. |
| 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 |