abort_packages_not_installed | Throw error if required packages are not installed. |
bootstrap_performance | Calculate a bootstrap confidence interval for the performance... |
bounds | Get the lower and upper bounds for an empirical confidence... |
calc_balanced_precision | Calculate balanced precision given actual and baseline... |
calc_baseline_precision | Calculate the fraction of positives, i.e. baseline precision... |
calc_mean_perf | Generic function to calculate mean performance curves for... |
calc_perf_bootstrap_split | Calculate performance for a single split from... |
calc_perf_metrics | Get performance metrics for test data |
calc_pvalue | Calculate the p-value for a permutation test |
change_to_num | Change columns to numeric if possible |
check_all | Check all params that don't return a value |
check_cat_feats | Check if any features are categorical |
check_corr_thresh | check that corr_thresh is either NULL or a number between 0... |
check_dataset | Check that the dataset is not empty and has more than 1... |
check_features | Check features |
check_group_partitions | Check the validity of the group_partitions list |
check_groups | Check grouping vector |
check_kfold | Check that kfold is an integer of reasonable size |
check_method | Check if the method is supported. If not, throws error. |
check_ntree | Check ntree |
check_outcome_column | Check that outcome column exists. Pick outcome column if not... |
check_outcome_value | Check that the outcome variable is valid. Pick outcome value... |
check_packages_installed | Check whether package(s) are installed |
check_perf_metric_function | Check perf_metric_function is NULL or a function |
check_perf_metric_name | Check perf_metric_name is NULL or a function |
check_permute | Check that permute is a logical |
check_remove_var | Check remove_var |
check_seed | check that the seed is either NA or a number |
check_training_frac | Check that the training fraction is between 0 and 1 |
check_training_indices | Check the validity of the training indices |
cluster_corr_mat | Cluster a matrix of correlated features |
collapse_correlated_features | Collapse correlated features |
combine_hp_performance | Combine hyperparameter performance metrics for multiple... |
compare_models | Perform permutation tests to compare the performance metric... |
create_grouped_data_partition | Split into train and test set while splitting by groups. When... |
create_grouped_k_multifolds | Splitting into folds for cross-validation when using groups |
define_cv | Define cross-validation scheme and training parameters |
find_permuted_perf_metric | Get permuted performance metric difference for a single... |
flatten_corr_mat | Flatten correlation matrix to pairs |
get_binary_corr_mat | Identify correlated features as a binary matrix |
get_caret_dummyvars_df | Get dummyvars dataframe (i.e. design matrix) |
get_caret_processed_df | Get preprocessed dataframe for continuous variables |
get_corr_feats | Identify correlated features |
get_difference | Calculate the difference in the mean of the metric for two... |
get_feature_importance | Get feature importance using the permutation method |
get_groups_from_clusters | Assign features to groups |
get_hp_performance | Get hyperparameter performance metrics |
get_hyperparams_from_df | Split hyperparameters dataframe into named lists for each... |
get_hyperparams_list | Set hyperparameters based on ML method and dataset... |
get_outcome_type | Get outcome type. |
get_partition_indices | Select indices to partition the data into training & testing... |
get_perf_metric_fn | Get default performance metric function |
get_perf_metric_name | Get default performance metric name |
get_performance_tbl | Get model performance metrics as a one-row tibble |
get_seeds_trainControl | Get seeds for 'caret::trainControl()' |
get_tuning_grid | Generate the tuning grid for tuning hyperparameters |
group_correlated_features | Group correlated features |
is_whole_number | Check whether a numeric vector contains whole numbers. |
keep_groups_in_cv_partitions | Whether groups can be kept together in partitions during... |
mikropml-package | mikropml: User-Friendly R Package for Robust Machine Learning... |
mutate_all_types | Mutate all columns with 'utils::type.convert()'.' |
otu_data_preproc | Mini OTU abundance dataset - preprocessed |
otu_mini_bin | Mini OTU abundance dataset |
otu_mini_bin_results_glmnet | Results from running the pipeline with L2 logistic regression... |
otu_mini_bin_results_rf | Results from running the pipeline with random forest on... |
otu_mini_bin_results_rpart2 | Results from running the pipeline with rpart2 on... |
otu_mini_bin_results_svmRadial | Results from running the pipeline with svmRadial on... |
otu_mini_bin_results_xgbTree | Results from running the pipeline with xbgTree on... |
otu_mini_cont_results_glmnet | Results from running the pipeline with glmnet on... |
otu_mini_cont_results_nocv | Results from running the pipeline with glmnet on... |
otu_mini_cv | Cross validation on 'train_data_mini' with grouped features. |
otu_mini_multi | Mini OTU abundance dataset with 3 categorical variables |
otu_mini_multi_group | Groups for otu_mini_multi |
otu_mini_multi_results_glmnet | Results from running the pipeline with glmnet on... |
otu_small | Small OTU abundance dataset |
pbtick | Update progress if the progress bar is not 'NULL'. |
permute_p_value | Calculated a permuted p-value comparing two models |
plot_curves | Plot ROC and PRC curves |
plot_hp_performance | Plot hyperparameter performance metrics |
plot_model_performance | Plot performance metrics for multiple ML runs with different... |
preprocess_data | Preprocess data prior to running machine learning |
process_cat_feats | Process categorical features |
process_cont_feats | Preprocess continuous features |
process_novar_feats | Process features with no variation |
radix_sort | Call 'sort()' with 'method = 'radix" |
randomize_feature_order | Randomize feature order to eliminate any position-dependent... |
reexports | caret contr.ltfr |
remove_singleton_columns | Remove columns appearing in only 'threshold' row(s) or fewer. |
replace_spaces | Replace spaces in all elements of a character vector with... |
rm_missing_outcome | Remove missing outcome values |
run_ml | Run the machine learning pipeline |
select_apply | Use future apply if available |
sensspec | Calculate and summarize performance for ROC and PRC plots |
set_hparams_glmnet | Set hyperparameters for regression models for use with glmnet |
set_hparams_rf | Set hyparameters for random forest models |
set_hparams_rpart2 | Set hyperparameters for decision tree models |
set_hparams_svmRadial | Set hyperparameters for SVM with radial kernel |
set_hparams_xgbTree | Set hyperparameters for SVM with radial kernel |
shared_ggprotos | Get plot layers shared by 'plot_mean_roc' and 'plot_mean_prc' |
shuffle_group | Shuffle the rows in a column |
split_outcome_features | Split dataset into outcome and features |
tidy_perf_data | Tidy the performance dataframe |
train_model | Train model using 'caret::train()'. |
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