oml

Math Library
Library oml
Module Oml . Statistics . Descriptive
include module type of Oml_descriptive
val mean : float array -> float
val median : float array -> float
val var : ?population_mean:float -> ?biased:bool -> float array -> float
val sd : ?population_mean:float -> ?biased:bool -> float array -> float
val ad : ?population:float -> ?center:[ `Mean | `Median ] -> float array -> float
val covariance : ?population_means:(float * float) -> ?biased:bool -> 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 : ?biased:bool -> float array -> float
val kurtosis : ?biased:bool -> 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 = [
| `Negative
| `Normal
| `Positive
| `Slightly_negative
| `Slightly_positive
]
val classify_skew : float array -> skew_classification
type kurtosis_classification = [
| `Fat
| `Normal
| `Skinny
| `Slightly_fat
| `Slightly_skinny
]
val classify_kurtosis : float array -> kurtosis_classification
type summary = {
size : int;
min : float;
max : float;
mean : float;
std : float;
var : float;
skew : float * skew_classification;
kurtosis : float * kurtosis_classification;
}
val summary : ?biased:bool -> 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
val cosine : float array -> float array -> float