Nothing
get_kern_matrix()
accessor function.train.samp
and test.samp
to kernL()
and iprior()
to easily split training and test samples for cross-validation.iprior_em_closed()
which caused lambda to expand together with the number of iterations.ggplot2
package.d
-degree polynomial kernel with offset c
.kernL()
, while still keeping support for the legacy .kernL()
function - although there are plans to phase out this in favour of the new one.summary
method for ipriorKernel2
objects. Canonical
, FBM
and Pearson
are now referred to as linear
, fbm
and pearson
, but there is backward compatability with the old references. parsm
option for interactions has been removed - it's hardly likely that this is ever useful.rootkern
option for Gaussian process regression has been removed. Should use specialised GPR software for this and keep this package for I-priors only.order
option to specify higher order terms has been removed in favour of polynomial kernels.control = list(restarts = TRUE)
. By default it will use the maximum number of available cores to fit the model in parallel from different random initial values.plot_fitted()
, plot_predict()
, and plot_iter()
.x
then H(x) = H1(x[1]) + ... + H_p(x[p])
. This is only true for Canonical kernel. Now correctly applies the FBM kernel using the norm function on each multivariate x_i
.iprobit
] (https://github.com/haziqjamil/iprobit) package. By using a probit link, the I-prior methodology is extended to categorical responses.iprobit
package. Added support for categorical response kernel loading.is.ipriorKernel()
and is.ipriorMod()
.iprior()
and kernL()
.ipriorMod
objects by not saving Psql
, Sl
, Hlam.mat
, and VarY.inv
. Although these are no longer stored within an ipriorMod
object, they can still be retrieved via the functions Hlam()
and vary()
.ipriorOptim()
or fbmOptim()
whereby standard errors could not be calculated.fbmOptim()
: Ability to specify an interval to search for, and also the maximum number of iterations for the initial EM step.str()
when printing ipriorKernel
objects.fbmOptim()
function to find optimum Hurst coefficient for fitting FBM I-prior models.kernel = "FBM,<value>"
.lambda
.summary()
output for now.sigma()
not being available from the stats
package prior to R v3.3.0. kernL()
.n > 1000
. This is mainly due to the matrix multiplication and data storing process when the EM initialises. See issue #20.predict()
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