class_docstr: Class for fitting the Nelson-Aalen estimate for the cumulative hazard.
  
  NelsonAalenFitter(alpha=0.05, nelson_aalen_smoothing=True)
  
  Parameters
  ----------
  alpha: float, optional (default=0.05)
      The alpha value associated with the confidence intervals.
  nelson_aalen_smoothing: bool, optional
      If the event times are naturally discrete (like discrete years, minutes, etc.)
    then it is advisable to turn this parameter to False. See [1], pg.84.
  
  Notes
  ------
  [1] Aalen, O., Borgan, O., Gjessing, H., 2008. Survival and Event History Analysis
  
  Attributes
  ----------
  cumulative_hazard_ : DataFrame
      The estimated cumulative hazard (with custom timeline if provided)
  confidence_interval_ : DataFrame
      The lower and upper confidence intervals for the cumulative hazard
  durations: array
      The durations provided
  event_observed: array
      The event_observed variable provided
  timeline: array
      The time line to use for plotting and indexing
  entry: array or None
      The entry array provided, or None
  event_table: DataFrame
      A summary of the life table
code: def _additive_f_smooth(self, population, deaths):
      cum_ = np.cumsum(1.0 / np.arange(1, np.max(population) + 1))
      return pd.Series(
          cum_[population - 1]
          - np.where(
              population - deaths - 1 >= 0,
              cum_[population - deaths - 1],
              0,
          ),
          index=population.index,
      )
  
imports: import warnings
  import numpy
  import pandas
  from lifelines.fitters import UnivariateFitter
  from lifelines.plotting import _plot_estimate
  from lifelines.exceptions import StatisticalWarning
  from lifelines.utils import _preprocess_inputs, _additive_estimate, epanechnikov_kernel, inv_normal_cdf, check_nans_or_infs, CensoringType, coalesce, _to_1d_array
label_desc: Use image node_id 7 for calling the NelsonAalenFitter obj's underlying member method code with example usage: obj._additive_f_smooth(population, deaths) and returns: pd