Fit a random forest model for classification or regression tasks.

Apply a function to each element of a list or vector while also providing the index or name of the element.

Apply a function to specified elements or positions within a list or vector.

Apply a function to corresponding elements of two lists or vectors and return a list of the results.

Apply a function to each element of a list or vector and return a list of the results.

Train and fit a neural network model.

Generate predictions from a fitted model.

Fit a Naive Bayes classifier to data.

Downsample input data by taking the maximum value over a specified window.

Perform 2D convolution on input data.

Flatten the input while maintaining the batch size.

Add a dropout layer to prevent overfitting.

Add a densely-connected neural network layer.

Install the Keras library and its dependencies.

Fit a generalized linear model via penalized maximum likelihood.

Combine multidimensional arrays along a specified dimension.

Impute missing values in a dataset using random forests.

Write imputed datasets to disk.

Impute missing data using multiple imputation.

Create kernel density plots.