| apply_acd | Function to apply Approximate Cumulative Distribution (ACD) |
| apply_shift_scale | Shift and scale vector x |
| art | Artificial dataset with continuous response |
| assign_df | Helper to assign degrees of freedom |
| backscale_matrix | Backscale Columns of a Matrix (Internal) |
| calculate_df | Helper to calculates the final degrees of freedom for the selected model |
| calculate_f_test | Function to compute F-statistic and p-value from deviances |
| calculate_lr_test | Function to calculate p-values for likelihood-ratio test |
| calculate_model_metrics | Function to compute model metrics to be used within 'mfp2' |
| calculate_number_fp_powers | Calculates the total number of fractional polynomial powers in adjustment variables. |
| calculate_standard_error | Helper function to compute standard error of a partial predictor |
| center_matrix | Simple function to center data |
| coef.mfp2 | Extract coefficients from object of class 'mfp2' |
| convert_powers_list_to_matrix | Helper to convert a nested list with same or different length into a matrix |
| create_dummy_variables | Simple function to create dummy variables for ordinal and nominal variables |
| create_fp_terms | Helper to create overview table of fp terms |
| deviance_gaussian | Deviance computations as used in mfp in stata |
| ensure_length | Helper function to ensure vectors have a specified length |
| find_best_fp1_for_acd | Function to fit univariable FP1 models for acd transformation |
| find_best_fpm_step | Function to find the best FP functions of given degree for a single variable |
| find_best_fp_cycle | Helper to run cycles of the mfp algorithm |
| find_best_fp_step | Function to estimate the best FP functions for a single variable |
| find_scale_factor | Function that calculates an integer used to scale predictor |
| find_shift_factor | Function that calculates a value used to shift predictor |
| fit_acd | Function to estimate approximate cumulative distribution (ACD) |
| fit_cox | Function that fits Cox proportional hazards models |
| fit_glm | Function that fits generalized linear models |
| fit_linear_step | Function to fit linear model for variable of interest |
| fit_mfp | Function for fitting a model using the MFP or MFPA algorithm |
| fit_model | Function that fits models supported by 'mfp2' |
| fit_null_step | Function to fit null model excluding variable of interest |
| fp | Helper to assign attributes to a variable undergoing FP-transformation |
| fp2 | Helper to assign attributes to a variable undergoing FP-transformation |
| fracplot | Plot response functions from a fitted 'mfp2' object |
| gbsg | Breast cancer dataset used in the Royston and Sauerbrei (2008) book. |
| generate_combinations_with_replacement | Helper function to generate combinations with replacement |
| generate_powers_acd | Function that generates a matrix of FP powers for any degree |
| generate_powers_fp | Function that generates a matrix of FP powers for any degree |
| generate_transformations_acd | Function to generate all requested FP transformations for a single variable |
| generate_transformations_fp | Function to generate all requested FP transformations for a single variable |
| get_selected_variable_names | Helper function to extract selected variables from fitted 'mfp2' object |
| mfp2 | Multivariable Fractional Polynomial Models with Extensions |
| mfp2.default | Multivariable Fractional Polynomial Models with Extensions |
| mfp2.formula | Multivariable Fractional Polynomial Models with Extensions |
| name_transformed_variables | Helper function to name transformed variables |
| order_variables | Helper to order variables for mfp2 algorithm |
| order_variables_by_significance | Helper to order variables for mfp2 algorithm |
| pima | Pima Indians dataset used in the Royston and Sauerbrei (2008) book. |
| plot_mfp | Plot response functions from a fitted 'mfp2' object |
| predict.mfp2 | Predict Method for 'mfp2' |
| prepare_newdata_for_predict | Helper function to prepare newdata for predict function |
| print.mfp2 | Print method for objects of class 'mfp2' |
| print_mfp_ic_step | Function for verbose printing of function selection procedure (FSP) |
| print_mfp_pvalue_step | Function for verbose printing of function selection procedure (FSP) |
| print_mfp_step | Function for verbose printing of function selection procedure (FSP) |
| prostate | Prostate cancer dataset used in the Royston and Sauerbrei (2008) book. |
| reset_acd | Helper to reset acd transformation for variables with few values |
| select_ic | Function selection procedure based on information criteria |
| select_ic_acd | Function selection procedure based on information criteria |
| select_linear | Helper to select between null and linear term for a single variable |
| select_ra2 | Function selection procedure based on closed testing procedure |
| select_ra2_acd | Function selection procedure for ACD based on closed testing procedure |
| summary.mfp2 | Summarizing 'mfp2' model fits |
| transform_data_step | Function to extract and transform adjustment variables |
| transform_matrix | Function to transform each column of matrix using final FP powers or acd |
| transform_vector_acd | Functions to transform a variable using fractional polynomial powers or acd |
| transform_vector_fp | Functions to transform a variable using fractional polynomial powers or acd |
| transform_vector_power | Simple function to transform vector by a single power |