| aicreg | Identify model based upon AIC criteria from a stepreg() putput |
| best.preds | Get the best models for the steps of a stepreg() fit |
| 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. |
| difftime1 | Get elapsed time in c(hour, minute, secs) |
| difftime2 | Output to console the elapsed and split times |
| 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.simdata | Generate example data |
| glmnetrll_1fold | Evaluate fit of leave out fold |
| glmnetr_devratio | Get Deviance ratio. |
| nested.glmnetr | Using nested cross validation, describe the fit of a cross validation informed relaxed lasso model fit. |
| 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. |
| preds_1 | Get predictors form a stepwise regression model. |
| 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 a nested.glmnetr() output object |
| summary.stepreg | Briefly summarize steps in a stepreg() output object, i.e. a stepwise regression fit |