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() functionality.
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