saddle-stats/org.saddle.stats

org.saddle.stats

package org.saddle.stats

Type members

Classlikes

class DoubleExpandingStats(r: Vec[Double]) extends VecExpandingStats[Double]
class DoubleStats(r: Vec[Double]) extends VecStats[Double]
class FrameStats[RX, CX, T](frame: Frame[RX, CX, T])(implicit evidence$1: ScalarTag[T])

Statistical methods made available to operate on columns of a Frame via enrichment.

Statistical methods made available to operate on columns of a Frame via enrichment.

Type parameters:
CX

Type of the column index elements

RX

Type of the row index elements

T

Type of the elements of the frame

Value parameters:
frame

The frame to enrich

class IntExpandingStats(r: Vec[Int]) extends VecExpandingStats[Int]
class IntStats(r: Vec[Int]) extends VecStats[Int]
class LongExpandingStats(r: Vec[Long]) extends VecExpandingStats[Long]
class LongStats(r: Vec[Long]) extends VecStats[Long]
class Mediator(winSz: Int)

Mediator is an auxiliary class for O(N log k) rolling median. It is inspired by AShelly's C99 implementation, which is (c) 2011 ashelly.myopenid.com and licensed under the MIT license: http://www.opensource.org/licenses/mit-license

Mediator is an auxiliary class for O(N log k) rolling median. It is inspired by AShelly's C99 implementation, which is (c) 2011 ashelly.myopenid.com and licensed under the MIT license: http://www.opensource.org/licenses/mit-license

Reference: http://stackoverflow.com/questions/5527437/rolling-median-in-c-turlach-implementation

class RollingCount[@specialized(Int, Long, Double) A] extends Vec[A] => Int
class RollingMean[@specialized(Int, Long, Double) A] extends Vec[A] => Double
class RollingMedian[@specialized(Int, Long, Double) A](winSz: Int, origv: Vec[A])(implicit evidence$13: ScalarTag[A], evidence$14: Numeric[A])
class RollingSum[@specialized(Int, Long, Double) A] extends Vec[A] => A
class SeriesExpandingStats[X, T](s: Series[X, T])(implicit evidence$1: ScalarTag[X], evidence$2: Order[X], evidence$3: () => T, evidence$4: ScalarTag[T])

Expanding statistical methods made available on numeric Series objects via enrichment. These methods scan over the Series and compute values over a specified historical window.

Expanding statistical methods made available on numeric Series objects via enrichment. These methods scan over the Series and compute values over a specified historical window.

Companion:
object
object SeriesExpandingStats
Companion:
class
class SeriesRollingStats[X, T](s: Series[X, T])(implicit evidence$1: ScalarTag[X], evidence$2: Order[X], evidence$3: () => T, evidence$4: ScalarTag[T])

Rolling statistical methods made available on numeric Series objects via enrichment. These methods scan over the Series and compute cumulative values.

Rolling statistical methods made available on numeric Series objects via enrichment. These methods scan over the Series and compute cumulative values.

Companion:
object
object SeriesRollingStats
Companion:
class
trait VecExpandingStats[@specialized(Int, Long, Double) A]

Expanding statistical methods made available on numeric Vec objects via enrichment. These methods scan over the Vec and compute cumulative values.

Expanding statistical methods made available on numeric Vec objects via enrichment. These methods scan over the Vec and compute cumulative values.

class VecRollingStats[@specialized(Int, Long, Double) A](v: Vec[A])(implicit evidence$1: ScalarTag[A], evidence$2: AddOp[A], evidence$3: SubOp[A], evidence$4: Numeric[A])

Rolling statistical methods made available on numeric Vec objects via enrichment. These methods scan over the Vec and compute values over a specified historical window.

Rolling statistical methods made available on numeric Vec objects via enrichment. These methods scan over the Vec and compute values over a specified historical window.

trait VecStats[@specialized(Int, Long, Double) A]

Statistical methods made available on numeric Vec objects via enrichment.

Statistical methods made available on numeric Vec objects via enrichment.

Types

type Series2Stats[T] = Series[_, T] => VecStats[T]
type Vec2ExpandingStats[T] = Vec[T] => VecExpandingStats[T]
type Vec2RollingStats[T] = Vec[T] => VecRollingStats[T]
type Vec2Stats[T] = Vec[T] => VecStats[T]

Implicits

Implicits

implicit def frameToStats[RX, CX, T : ScalarTag](f: Frame[RX, CX, T]): FrameStats[RX, CX, T]

Enrich a Frame to provide statistical methods

Enrich a Frame to provide statistical methods

implicit def seriesToExpandingStats[X : Order, T : ScalarTag](s: Series[X, T]): SeriesExpandingStats[X, T]

Enrich Series with expanding stats

Enrich Series with expanding stats

Value parameters:
s

Series[_, T]

implicit def seriesToRollingStats[X : Order, T : ScalarTag](s: Series[X, T]): SeriesRollingStats[X, T]

Enrich Series with rolling stats

Enrich Series with rolling stats

Value parameters:
s

Series[_, T]

implicit def seriesToStats[T : Vec2Stats](s: Series[_, T]): VecStats[T]

Enrich Series with basic stats

Enrich Series with basic stats

Value parameters:
s

Series[_, T]

implicit def vecToDoubleExpStats(s: Vec[Double]): VecExpandingStats[Double]
implicit def vecToDoubleRollingStats(s: Vec[Double]): VecRollingStats[Double]
implicit def vecToDoubleStats(s: Vec[Double]): VecStats[Double]
implicit def vecToIntExpStats(s: Vec[Int]): VecExpandingStats[Int]
implicit def vecToIntRollingStats(s: Vec[Int]): VecRollingStats[Int]
implicit def vecToIntStats(s: Vec[Int]): VecStats[Int]
implicit def vecToLongExpStats(s: Vec[Long]): VecExpandingStats[Long]
implicit def vecToLongRollingStats(s: Vec[Long]): VecRollingStats[Long]
implicit def vecToLongStats(s: Vec[Long]): VecStats[Long]