# Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models

## Documentation for package ‘glmnetr’ version 0.6-2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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