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val mean : float array -> float
val median : float array -> float
val var : float array -> float
val unbiased_var : float array -> float
val population_var : float -> float array -> float
val covariance : float array -> float array -> float
val correlation : float array -> float array -> float
val autocorrelation : int -> float array -> float
val moment : int -> float array -> float
val skew : float array -> float
val unbiased_skew : float array -> float
val kurtosis : float array -> float
val unbiased_kurtosis : float array -> float
val var_standard_error : float array -> float
val skew_standard_error : float array -> float
val kurtosis_standard_error : float array -> float
val var_statistic : float array -> float
val skew_statistic : float array -> float
val kurtosis_statistic : float array -> float
type skew_classification = [
  1. | `Negative
  2. | `Normal
  3. | `Positive
  4. | `Slightly_negative
  5. | `Slightly_positive
]
val classify_skew : float array -> skew_classification
type kurtosis_classification = [
  1. | `Fat
  2. | `Normal
  3. | `Skinny
  4. | `Slightly_fat
  5. | `Slightly_skinny
]
val classify_kurtosis : float array -> kurtosis_classification
type summary = {
  1. size : int;
  2. min : float;
  3. max : float;
  4. mean : float;
  5. std : float;
  6. var : float;
  7. skew : float * skew_classification;
  8. kurtosis : float * kurtosis_classification;
}
val unbiased_summary : float array -> summary
val histogram : [ `Buckets of int | `Specific of float array | `Width of float ] -> float array -> (float * int) array
val geometric_mean : float array -> float
val harmonic_mean : float array -> float
val spearman : float array -> float array -> float