conIdPar 
Parameter controlling the number of neighbors to be used
for identifying and validating constant models. conIdPar can assume
different forms:
conIdPar=c(idm0,idM0,valM0) : In this case,
idm0:idM0 is the range in which the best number of
neighbors is searched when identifying the local polynomial
models of degree 0 and where valM0 is the maximum
number of neighbors used for their validation. This means
that the constant models identified with k neighbors,
are validated on the first v neighbors, where
v=min(k,valM0) . If valM0=0 , valM0 is set
to idMO : see next case for details.
conIdPar=c(idm0,idM0) : Here idm0 and
idM0 have the same role as in previous case, and
valM0 is by default set to idM0 : each model is
validated on all the neighbors used in identification.
conIdPar=p : Here idmO and idMO are
obtained according to the following formulas: idm0=3
and idMX=5*p . Recommended choice: p=1 . As far as
the quantity valM0 is concerned, it gets the default
value as in previous case.
conIdPar=NULL : No constant model is considered.

linIdPar 
Parameter controlling the number of neighbors to be used
for identifying and validating linear models. linIdPar can assume
different forms:
linIdPar=c(idm1,idM1,valM1) : In this case,
idm1:idM1 is the range in which the best number of
neighbors is searched when identifying the local polynomial
models of degree 1 and where valM1 is the maximum
number of neighbors used for their validation. This means
that the linear models identified with k neighbors, are
validated on the first v neighbors, where
v=min(k,valM1) . If valM1=0 , valM1 is set
to idM1 : see next case for details.
linIdPar=c(idm1,idM1) : Here
idm1 and idM1 have the same role as in previous
case, and valM1 is by default set to idM1 : each
model is validated on all the neighbors used in identification.
linIdPar=p : Here idmO and idMO are
obtained according to the following formulas:
idm1=3*noPar and idM1=5*p*noPar , where
noPar=nx+1 is the number of parameter of the polynomial
model of degree 1, and nx is the dimensionality of the
input space. Recommended choice: p=1 . As far as the
quantity valM1 is concerned, it gets the default value
as in previous case.
linIdPar=NULL : No linear model is considered.

quaIdPar 
Parameter controlling the number of neighbors to be
used for identifying and validating quadratic
models. quaIdPar can assume different forms:
quaIdPar=c(idm2,idM2,valM2) : In this case,
idm2:idM2 is the range in which the best number of
neighbors is searched when identifying the local polynomial
models of degree 2 and where valM2 is the maximum
number of neighbors used for their validation. This means
that the quadratic models identified with k neighbors, are
validated on the first v neighbors, where
v=min(k,valM2) . If valM2=0 , valM2 is set
to idM2 : see next case for details.
quaIdPar=c(idm2,idM2) : Here
idm2 and idM2 have the same role as in previous
case, and valM2 is by default set to idM2 : each
model is validated on all the neighbors used in identification.
quaIdPar=p : Here idmO and idMO are
obtained according to the following formulas:
idm2=3*noPar and idM2=5*p*noPar , where in this
case the number of parameters is
noPar=(nx+1)*(nx+2)/2 , and nx is the
dimensionality of the input space. Recommended choice:
p=1 . As far as the quantity valM2 is concerned,
it gets the default value as in previous case.
quaIdPar=NULL : No quadratic model is considered.

distance 
The distance metric: can be manhattan or
euclidean .

metric 
Vector of n elements. Weights used to evaluate
the distance between query point and neighbors.

cmbPar 
Parameter controlling the local combination of
models. cmbPar can assume different forms:
cmbPar=c(cmb0,cmb1,cmb2) : In this case, cmbX
is the number of polynomial models of degree X that will
be included in the local combination. Each local model will be
therfore a combination of the best cmb0
models of degree 0, the best cmb1 models of
degree 1, and the best cmb2 models of degree 2
identified as specified by idPar .
cmbPar=cmb : Here cmb is the number of models
that will be combined, disregarding any constraint on the
degree of the models that will be considered. Each local model
will be therfore a combination of the best cmb
models, identified as specified by id_par .

lambda 
Initialization of the diagonal elements of the local
variance/covariance matrix for Ridge Regression.
