balance_panel           Balance panel data
bin_data                Discretize continuous variables into bins
clean_strings           Clean and standardize character columns
condextr                Remove outliers using point-by-point weighed
                        outlier removal by conditional extremum
create_lags             Create lagged variables
data                    Example data (particle number concentrations in
                        SMEAR I Varrio forest)
data1                   Example data (aggregated particle number
                        concentrations, SMEAR I Varrio forest)
data_report             Generate a simple data quality report
dataprep                Data preprocessing with multiple steps in one
                        function
dataprep-package        dataprep: Fast, Efficient, and Versatile Data
                        Preprocessing and Reshaping with 'C++',
                        'OpenMP' & 'SIMD'
day_night_flag          Day/night flag
dcast                   Cast a long-format data.frame into a wide
                        format
decompose_ts            Simple time series decomposition
deduplicate             Remove duplicate observations
descdata                Fast descriptive statistics
descplot                View descriptive statistics via plot
detect_outliers         Detect outliers using multiple methods
detrend_ts              Remove linear trend from time series
drift_detect            Sensor drift detection
dry_run                 Simulate preprocessing and report changes
                        without modifying data
encode_categorical      Encode categorical variables
filter_high_cor         Remove highly correlated variables
filter_low_var          Remove low-variance (near-constant) variables
impute_missing          Impute missing values
log_returns             Logarithmic returns for financial time series
melt                    Fast wide-to-long data reshaping with flexible
                        ID/measure specification
na_diagnose             Diagnose missing value patterns in data
obsedele                Delete observations with excessive consecutive
                        missing values
optisolu                Find optimal combination of interval and times
                        for condextr
percdata                Calculate top and bottom percentiles of
                        selected variables
percoutl                Traditional percentile-based outlier removal
percplot                Plot top and bottom percentiles of selected
                        variables
phys_filter             Physical limit filtering
prep_fit                Build a preprocessing plan on training data to
                        prevent data leakage
prep_transform          Apply a preprocessing plan to new data
remove_diurnal_cycle    Remove diurnal cycle
resample_time           Resample time series to a coarser period
roll_apply              Apply rolling window statistics
sample_data             Random sampling with optional stratification
season_flag             Season flag
shorvalu                Interpolation with values to refer to within
                        short periods
transform_data          Transform and standardize numeric variables
validate_data           Validate data against a set of rules
varidele                Delete variables containing too many missing
                        values
winsorize               Winsorize outliers by capping extreme values
zerona                  Turn zeros to missing values
