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funModeling (version 1.9.6)

Exploratory Data Analysis and Data Preparation Tool-Box

Description

Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book () are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.

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Version

Install

install.packages('funModeling')

Monthly Downloads

959

Version

1.9.6

License

GPL-2

Maintainer

Pablo Casas

Last Published

February 18th, 2026

Functions in funModeling (1.9.6)

tukey_outlier

Tukey Outlier Threshold
freq

Frequency table for categorical variables
data_golf

Play golf
var_rank_info

Importance variable ranking based on information theory
infor_magic

Computes several information theory metrics between two vectors
data_integrity_model

Check data integrity model
data_integrity

Data integrity
status

Get a summary for the given data frame (o vector).
hampel_outlier

Hampel Outlier Threshold
get_sample

Sampling training and test data
heart_disease

Heart Disease Data
range01

Transform a variable into the [0-1] range
gain_ratio

Gain ratio
discretize_df

Discretize a data frame
prep_outliers

Outliers Data Preparation
gain_lift

Generates lift and cumulative gain performance table and plot
export_plot

Export plot to jpeg file
profiling_num

Profiling numerical data
fibonacci

Fibonacci series
metadata_models

Metadata models data integrity
information_gain

Information gain
funModeling-package

funModeling: Exploratory data analysis, data preparation and model performance
plot_num

Plotting numerical data
plotar

Correlation plots
concatenate_n_vars

Concatenate 'N' variables
compare_df

Compare two data frames by keys
data_country

People with flu data
categ_analysis

Profiling analysis of categorical vs. target variable
auto_grouping

Reduce cardinality in categorical variable by automatic grouping
convert_df_to_categoric

Convert every column in a data frame to character
correlation_table

Get correlation against target variable
cross_plot

Cross-plotting input variable vs. target variable
desc_groups

Profiling categorical variable
equal_freq

Equal frequency binning
coord_plot

Coordinate plot
entropy_2

Computes the entropy between two variables
discretize_rgr

Variable discretization by gain ratio maximization
discretize_get_bins

Get the data frame thresholds for discretization
desc_groups_rank

Profiling categorical variable (rank)
df_status

Get a summary for the given data frame (o vector).
v_compare

Compare two vectors