old_contents: import math
  
  
  def percentile(values, percent, key=lambda x: x):
      """
      Find the percentile of a list of values.
  
      Params:
          values (list): Sorted list of values.
          percent (float): A value from 0.0 to 1.0.
          key (function): Optional key function to compute value from each value on list.
  
      Returns:
          The percentile of the values.
      """
      if not values:
          return None
  
      k = (len(values) - 1) * percent
      floor = math.floor(k)
      ceil = math.ceil(k)
      if floor == ceil:
          return key(values[int(k)])
  
      d0 = key(values[int(floor)]) * (ceil - k)
      d1 = key(values[int(ceil)]) * (k - floor)
  
      return d0 + d1
  
  
  def filter_dict_by_iqr(dictionary):
      """
      Returns a new dictionary filtering values outside of the interquartile range.
  
      Params:
          dictionary (dict): Dictionary  to be filtered.
  
      Returns:
          A new dictionary without items outside of the interquartile range.
      """
      filtered_dict = {}
      values = sorted(set(dictionary.values()))
  
      first_quartile = percentile(values, 0.25)
      second_quartile = percentile(values, 0.75)
  
      for key in dictionary:
          if first_quartile <= dictionary[key] <= second_quartile:
              filtered_dict[key] = dictionary[key]
  
      return filtered_dict
  
  
  def rootLogLikelihoodRatio(a, b, c, d):
      """
      Calculates the root log-likelihood ratio for two events.
  
      This implementation is based on LLR interpretation provided in the following
      paper (see table and equations in page 7):
  
      Java, Akshay, et al. "Why we twitter: understanding microblogging usage and
      communities." Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on
      Web mining and social network analysis. ACM, 2007.
      Available at: http://aisl.umbc.edu/resources/369.pdf
  
      Params:
          a (int): Frequency of token of interest in dataset A.
          b (int): Frequency of token of interest in dataset B.
          c (int): Total number of observations in dataset A.
          d (int): Total number of observations in dataset B.
  
      Returns:
          The root log-likelihood ratio.
      """
      e1 = c * (a + b) / (c + d)
      e2 = d * (a + b) / (c + d)
  
      # Avoid division zero by replacing a or b by 1 if equal to 0
      result = math.sqrt(2 * (a * math.log(a / e1 or 1) + b * math.log(b / e2 or 1)))
  
      if a / c < b / d:
          result = -result
  
      return result
new_contents: import math
  import re
  
  
  def percentile(values, percent, key=lambda x: x):
      """
      Find the percentile of a list of values.
  
      Params:
          values (list): Sorted list of values.
          percent (float): A value from 0.0 to 1.0.
          key (function): Optional key function to compute value from each value on list.
  
      Returns:
          The percentile of the values.
      """
      if not values:
          return None
  
      k = (len(values) - 1) * percent
      floor = math.floor(k)
      ceil = math.ceil(k)
      if floor == ceil:
          return key(values[int(k)])
  
      d0 = key(values[int(floor)]) * (ceil - k)
      d1 = key(values[int(ceil)]) * (k - floor)
  
      return d0 + d1
  
  
  def filter_dict_by_iqr(dictionary):
      """
      Returns a new dictionary filtering values outside of the interquartile range.
  
      Params:
          dictionary (dict): Dictionary  to be filtered.
  
      Returns:
          A new dictionary without items outside of the interquartile range.
      """
      filtered_dict = {}
      values = sorted(set(dictionary.values()))
  
      first_quartile = percentile(values, 0.25)
      second_quartile = percentile(values, 0.75)
  
      for key in dictionary:
          if first_quartile <= dictionary[key] <= second_quartile:
              filtered_dict[key] = dictionary[key]
  
      return filtered_dict
  
  
  def rootLogLikelihoodRatio(a, b, c, d):
      """
      Calculates the root log-likelihood ratio for two events.
  
      This implementation is based on LLR interpretation provided in the following
      paper (see table and equations in page 7):
  
      Java, Akshay, et al. "Why we twitter: understanding microblogging usage and
      communities." Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on
      Web mining and social network analysis. ACM, 2007.
      Available at: http://aisl.umbc.edu/resources/369.pdf
  
      Params:
          a (int): Frequency of token of interest in dataset A.
          b (int): Frequency of token of interest in dataset B.
          c (int): Total number of observations in dataset A.
          d (int): Total number of observations in dataset B.
  
      Returns:
          The root log-likelihood ratio.
      """
      e1 = c * (a + b) / (c + d)
      e2 = d * (a + b) / (c + d)
  
      # Avoid division zero by replacing a or b by 1 if equal to 0
      result = math.sqrt(2 * (a * math.log(a / e1 or 1) + b * math.log(b / e2 or 1)))
  
      if a / c < b / d:
          result = -result
  
      return result
  
  
  def remove_twitter_mentions(text):
      return re.sub(r'@([A-Za-z0-9_]+)', '', text)
subject: Add a function to remove Twitter mentions from a text