module Math.IRT.MLE.Truncated
( DF (..)
, MLEResult (..)
, mleEst
) where
import Data.Default.Class
import Statistics.Distribution
import Math.IRT.Internal.Distribution
import Math.IRT.Internal.LogLikelihood
import Math.IRT.MLE.Internal.Generic
data DF = DF { steps :: !Int
, thetaEstimate :: !Double
, lower_bound :: !Double
, upper_bound :: !Double }
instance Default DF where
def = DF 10 0.0 (3.5) 3.5
mleEst :: (Distribution d, ContDistr d, DensityDeriv d, LogLikelihood d) => DF -> [Bool] -> [d] -> MLEResult
mleEst (DF n th lb ub) rs params =
let res = generic_mleEst rs params n th
in res { theta = min ub $ max lb $ theta res }