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pendensity (version 0.2.5)

Density Estimation with a Penalized Mixture Approach

Description

Estimating penalized (conditional) densities

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Version

Install

install.packages('pendensity')

Monthly Downloads

233

Version

0.2.5

License

GPL (>= 2)

Maintainer

Christian Schellhase

Last Published

February 22nd, 2013

Functions in pendensity (0.2.5)

new.lambda

Calculating new penalty parameter lambda
new.beta.val

Calculating the new parameter beta
pendensity-package

The package 'pendensity' offers routines for estimating penalized unconditional and conditional (on factor groups) densities.
print.pendensity

Printing the main results of the (conditional) penalized density estimation
Derv1

Calculating the first derivative of the pendensity likelihood function w.r.t. parameter beta
my.positive.definite.solve

my.positive.definite.solve
test.equal

Testing pairwise equality of densities
DeutscheBank

Daily final prices (DAX) of the German stock Deutsche Bank in the years 2006 and 2007
variance.par

Calculating the variance of the parameters
pen.log.like

Calculating the log likelihood
variance.val

Calculating variance and standard deviance of each observation.
ck

Calculating the actual weights ck
marg.likelihood

Calculating the marginal likelihood
pendensity

Calculating penalized density
Derv2

Calculating the second order derivative with and without penalty
bias.par

Calculating the bias of the parameter beta
plot.pendensity

Plotting estimated penalized densities
f.hat

Calculating the actual fitted values 'f.hat' of the estimated density function f for the response y
L.mat

Calculates the difference matrix of order m
distr.func

These functions are used for calculating the empirical and theoretical distribution functions.
my.bspline

my.bspline
Allianz

Daily final prices (DAX) of the German stock Allianz in the years 2006 and 2007
D.m

Calculating the penalty matrix
my.AIC

Calculating the AIC value
pendenForm

Formula interpretation and data transfer