Copyright	(c) 2020 Ximin Luo
License	BSD3
Maintainer	infinity0@pwned.gg
Stability	experimental
Portability	portable
Safe Haskell	Safe-Inferred
Language	Haskell2010

Statistics.Distribution.Lognormal

Contents

Constructors

Description

The log normal distribution. This is a continuous probability distribution that describes data whose log is clustered around a mean. For example, the multiplicative product of many independent positive random variables.

Synopsis

data LognormalDistribution
lognormalDistr :: Double -> Double -> LognormalDistribution
lognormalDistrErr :: Double -> Double -> Either String LognormalDistribution
lognormalDistrMeanStddevErr :: Double -> Double -> Either String LognormalDistribution
lognormalStandard :: LognormalDistribution

Documentation

data LognormalDistribution Source #

The lognormal distribution.

Instances

Instances details

FromJSON LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods parseJSON :: Value -> Parser LognormalDistribution # parseJSONList :: Value -> Parser [LognormalDistribution] #
ToJSON LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods toJSON :: LognormalDistribution -> Value # toEncoding :: LognormalDistribution -> Encoding # toJSONList :: [LognormalDistribution] -> Value # toEncodingList :: [LognormalDistribution] -> Encoding #
Data LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> LognormalDistribution -> c LognormalDistribution # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c LognormalDistribution # toConstr :: LognormalDistribution -> Constr # dataTypeOf :: LognormalDistribution -> DataType # dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c LognormalDistribution) # dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c LognormalDistribution) # gmapT :: (forall b. Data b => b -> b) -> LognormalDistribution -> LognormalDistribution # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> LognormalDistribution -> r # gmapQr :: forall r r'. (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> LognormalDistribution -> r # gmapQ :: (forall d. Data d => d -> u) -> LognormalDistribution -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> LognormalDistribution -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> LognormalDistribution -> m LognormalDistribution # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> LognormalDistribution -> m LognormalDistribution # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> LognormalDistribution -> m LognormalDistribution #
Generic LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Associated Types type Rep LognormalDistribution :: Type -> Type # Methods from :: LognormalDistribution -> Rep LognormalDistribution x # to :: Rep LognormalDistribution x -> LognormalDistribution #
Read LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods readsPrec :: Int -> ReadS LognormalDistribution # readList :: ReadS [LognormalDistribution] # readPrec :: ReadPrec LognormalDistribution # readListPrec :: ReadPrec [LognormalDistribution] #
Show LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods showsPrec :: Int -> LognormalDistribution -> ShowS # show :: LognormalDistribution -> String # showList :: [LognormalDistribution] -> ShowS #
Binary LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods put :: LognormalDistribution -> Put # get :: Get LognormalDistribution # putList :: [LognormalDistribution] -> Put #
Eq LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods (==) :: LognormalDistribution -> LognormalDistribution -> Bool # (/=) :: LognormalDistribution -> LognormalDistribution -> Bool #
ContDistr LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods density :: LognormalDistribution -> Double -> Double Source # logDensity :: LognormalDistribution -> Double -> Double Source # quantile :: LognormalDistribution -> Double -> Double Source # complQuantile :: LognormalDistribution -> Double -> Double Source #
ContGen LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods genContVar :: StatefulGen g m => LognormalDistribution -> g -> m Double Source #
Distribution LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods cumulative :: LognormalDistribution -> Double -> Double Source # complCumulative :: LognormalDistribution -> Double -> Double Source #
Entropy LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods entropy :: LognormalDistribution -> Double Source #
MaybeEntropy LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods maybeEntropy :: LognormalDistribution -> Maybe Double Source #
MaybeMean LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods maybeMean :: LognormalDistribution -> Maybe Double Source #
MaybeVariance LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods maybeVariance :: LognormalDistribution -> Maybe Double Source # maybeStdDev :: LognormalDistribution -> Maybe Double Source #
Mean LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods mean :: LognormalDistribution -> Double Source #
Variance LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal Methods variance :: LognormalDistribution -> Double Source # stdDev :: LognormalDistribution -> Double Source #
FromSample LognormalDistribution Double Source #	Variance is estimated using maximum likelihood method (biased estimation) over the log of the data. Returns `Nothing` if sample contains less than one element or variance is zero (all elements are equal)
Instance details Defined in Statistics.Distribution.Lognormal Methods fromSample :: Vector v Double => v Double -> Maybe LognormalDistribution Source #
type Rep LognormalDistribution Source #
Instance details Defined in Statistics.Distribution.Lognormal type Rep LognormalDistribution = D1 ('MetaData "LognormalDistribution" "Statistics.Distribution.Lognormal" "statistics-0.16.2.0-Iazw6pxXVUYHaurUiPE2b2" 'True) (C1 ('MetaCons "LND" 'PrefixI 'False) (S1 ('MetaSel ('Nothing :: Maybe Symbol) 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 NormalDistribution)))

Constructors

lognormalDistr Source #

Arguments

:: Double	Mu
-> Double	Sigma
-> LognormalDistribution

Create log normal distribution from parameters.

lognormalDistrErr Source #

Arguments

:: Double	Mu
-> Double	Sigma
-> Either String LognormalDistribution

Create log normal distribution from parameters.

lognormalDistrMeanStddevErr Source #

Arguments

:: Double	Mu
-> Double	Sigma
-> Either String LognormalDistribution

Create log normal distribution from mean and standard deviation.

lognormalStandard :: LognormalDistribution Source #

Standard log normal distribution with mu 0 and sigma 1.

Mean is sqrt e and variance is (e - 1) * e.