d/p/q/rZANVASIM(), d/p/q/rOANVASIM(), and
d/p/q/rZOANVASIM(). The established numbered names remain
available. This correspondence allows likelihood-based methods such as
vcov.gamlss() to locate each family’s density
function.ZANVASIM(), a zero-adjusted normal-kernel Vasicek
mean family, with nu modeling the probability at zero.OANVASIM(), a one-adjusted normal-kernel Vasicek
mean family, with nu modeling the probability at one.ZOANVASIM(), a zero-and-one-adjusted
normal-kernel Vasicek mean family. In this family, nu is
the probability at zero and tau is the conditional
probability at one among nonzero observations.d0NVASIM(),
p0NVASIM(), q0NVASIM(),
r0NVASIM(), d1NVASIM(),
p1NVASIM(), q1NVASIM(),
r1NVASIM(), d01NVASIM(),
p01NVASIM(), q01NVASIM(), and
r01NVASIM() functions.dpqr-0NvasicekM.R, dpqr-1NvasicekM.R, and
dpqr-01NvasicekM.R.ZANVASIM, OANVASIM, and
ZOANVASIM.x and q and
distinguished the boundary behavior of the zero-adjusted, one-adjusted,
and zero-and-one-adjusted distributions.tau parameter in
ZOANVASIM from the fixed quantile level used by
NVASIQ and LVASIQ.OANVASIM and
BEOI using the one-inflated accuracy1 response
from the ReadingSkills data.VASIM to NVASIM.VASIQ to NVASIQ.LVASIQ for the logistic-kernel quantile
distribution functions.N or L) and parameterization
(M or Q) explicitly.NVASIQ() does not accept tau as an
argument. For GAMLSS fitting, tau must be defined as a
scalar variable in the global environment.dNVASIQ(),
pNVASIQ(), qNVASIQ(), and
rNVASIQ() continue to accept tau
explicitly.LVASIQ() GAMLSS family for conditional
quantile regression with the logistic-kernel Vasicek distribution.mu and sigma; model fitting does
not use numerical differentiation.qVASIM().log.p handling in all quantile
functions.variance components of VASIM() and
VASIQ().VASIQ() and removed dependence on a global tau
object.mu in
VASIQ().dpqr-vasicekmean.R and
dpqr-vasicekquant.R.variance = function(mu, sigma) in
the VASIM family.variance = function(mu, sigma) in
the VASIQ family.
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