def fit(
        self,
        durations,
        event_observed,
        event_of_interest,
        timeline=None,
        entry=None,
        label="AJ_estimate",
        alpha=None,
        ci_labels=None,
        weights=None,
    ):  # pylint: disable=too-many-arguments,too-many-locals
        """
        Parameters
        ----------
          durations: an array or pd.Series of length n -- duration of subject was observed for
          event_observed: an array, or pd.Series, of length n. Integer indicator of distinct events. Must be
             only positive integers, where 0 indicates censoring.
          event_of_interest: integer -- indicator for event of interest. All other integers are considered competing events
             Ex) event_observed contains 0, 1, 2 where 0:censored, 1:lung cancer, and 2:death. If event_of_interest=1, then death (2)
             is considered a competing event. The returned cumulative incidence function corresponds to risk of lung cancer
          timeline: return the best estimate at the values in timelines (positively increasing)
          entry: an array, or pd.Series, of length n -- relative time when a subject entered the study. This is
             useful for left-truncated (not left-censored) observations. If None, all members of the population
             were born at time 0.
          label: a string to name the column of the estimate.
          alpha: the alpha value in the confidence intervals. Overrides the initializing
             alpha for this call to fit only.
          ci_labels: add custom column names to the generated confidence intervals
                as a length-2 list: [<lower-bound name>, <upper-bound name>]. Default: <label>_lower_<1-alpha/2>
          weights: n array, or pd.Series, of length n, if providing a weighted dataset. For example, instead
              of providing every subject as a single element of `durations` and `event_observed`, one could
              weigh subject differently.

        Returns
        -------
        self : AalenJohansenFitter
          self, with new properties like ``cumulative_incidence_``.
        """

        self._censoring_type = CensoringType.RIGHT

        # Checking for tied event times
        ties = self._check_for_duplicates(durations=durations, events=event_observed)

        if ties:
            warnings.warn(
                dedent(
                    """Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.
                To resolve ties, data is randomly jittered."""
                ),
                Warning,
            )
            durations = self._jitter(
                durations=pd.Series(durations),
                event=pd.Series(event_observed),
                jitter_level=self._jitter_level,
                seed=self._seed,
            )

        alpha = alpha if alpha else self.alpha

        # Creating label for event of interest & indicator for that event
        event_of_interest = int(event_of_interest)
        cmprisk_label = "CIF_" + str(event_of_interest)
        self.label_cmprisk = "observed_" + str(event_of_interest)

        # Fitting Kaplan-Meier for either event of interest OR competing risk
        km = KaplanMeierFitter().fit(
            durations, event_observed=event_observed, timeline=timeline, entry=entry, weights=weights
        )
        aj = km.event_table
        aj["overall_survival"] = km.survival_function_
        aj["lagged_overall_survival"] = aj["overall_survival"].shift()

        # Setting up table for calculations and to return to user
        event_spec = pd.Series(event_observed) == event_of_interest
        self.durations, self.event_observed, *_, event_table = _preprocess_inputs(
            durations=durations, event_observed=event_spec, timeline=timeline, entry=entry, weights=weights
        )
        event_spec_times = event_table["observed"]
        event_spec_times = event_spec_times.rename(self.label_cmprisk)
        aj = pd.concat([aj, event_spec_times], axis=1).reset_index()

        # Estimator of Cumulative Incidence (Density) Function
        aj[cmprisk_label] = (aj[self.label_cmprisk] / aj["at_risk"] * aj["lagged_overall_survival"]).cumsum()
        aj.loc[0, cmprisk_label] = 0  # Setting initial CIF to be zero
        aj = aj.set_index("event_at")

        # Setting attributes
        self._estimation_method = "cumulative_density_"
        self._estimate_name = "cumulative_density_"
        self._predict_label = label
        self._update_docstrings()

        self._label = label
        self.cumulative_density_ = pd.DataFrame(aj[cmprisk_label])

        # Technically, cumulative incidence, but consistent with KaplanMeierFitter
        self.event_table = aj[
            ["removed", "observed", self.label_cmprisk, "censored", "entrance", "at_risk"]
        ]  # Event table

        if self._calc_var:
            self.variance_, self.confidence_interval_ = self._bounds(
                aj["lagged_overall_survival"], alpha=alpha, ci_labels=ci_labels
            )
        else:
            self.variance_, self.confidence_interval_ = None, None

        return self
Parameters
        ----------
          durations: an array or pd.Series of length n -- duration of subject was observed for
          event_observed: an array, or pd.Series, of length n. Integer indicator of distinct events. Must be
             only positive integers, where 0 indicates censoring.
          event_of_interest: integer -- indicator for event of interest. All other integers are considered competing events
             Ex) event_observed contains 0, 1, 2 where 0:censored, 1:lung cancer, and 2:death. If event_of_interest=1, then death (2)
             is considered a competing event. The returned cumulative incidence function corresponds to risk of lung cancer
          timeline: return the best estimate at the values in timelines (positively increasing)
          entry: an array, or pd.Series, of length n -- relative time when a subject entered the study. This is
             useful for left-truncated (not left-censored) observations. If None, all members of the population
             were born at time 0.
          label: a string to name the column of the estimate.
          alpha: the alpha value in the confidence intervals. Overrides the initializing
             alpha for this call to fit only.
          ci_labels: add custom column names to the generated confidence intervals
                as a length-2 list: [<lower-bound name>, <upper-bound name>]. Default: <label>_lower_<1-alpha/2>
          weights: n array, or pd.Series, of length n, if providing a weighted dataset. For example, instead
              of providing every subject as a single element of `durations` and `event_observed`, one could
              weigh subject differently.

        Returns
        -------
        self : AalenJohansenFitter
          self, with new properties like ``cumulative_incidence_``.