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v0.7.4
- Small update to fix CRAN issues.
v0.7.2
- Updated
get_kern_matrix()
accessor function.
- Fixed bug in delta method conversion of standard errors in direct
optimisation.
- Updated references and README.
v0.7.1
- Modification to centering of SE and polynomial kernels.
- Added option
train.samp
and test.samp
to
kernL()
and iprior()
to easily split training
and test samples for cross-validation.
- Added a function to perform k-fold cross validation experiments for
I-prior models.
- Fixed minor bug in
iprior_em_closed()
which caused
lambda to expand together with the number of iterations.
- Fixed incorrect calculation of polynomial kernel.
- Removed all legacy functions.
- Updated vignette.
- Added vignette for cross-validation function.
v0.7.0
- This udpate provides a complete redesign of the internals of
the package. There are more kernels supported, new estimation methods,
and plots are done using the
ggplot2
package.
- Enhanced the methods and calculations for the linear (canonical)
kernel, the fractional Brownian motion kernel, and the Pearson
kernel.
- Added support for the squared exponential kernel and the
d
-degree polynomial kernel with offset c
.
- Newly redesigned kernel loader function
kernL()
, while
still keeping support for the legacy .kernL()
function -
although there are plans to phase out this in favour of the new
one.
- There is now a
summary
method for
ipriorKernel2
objects.
- The legacy kernels
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.
- The package now supports the following estimation methods:
- Direct minimisation of the marginal deviance;
- EM algorithm (efficient closed-form version and the “regular”
version);
- Combination of direct and EM methods;
- A fixed estimation method to obtain the posterior regression
function without estimating any hyperparameters; and
- The Nystrom kernel approximation method.
- Parallel restarts is supported via
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.
- New plot functions added:
plot_fitted()
,
plot_predict()
, and plot_iter()
.
- Updated documentation throughout.
- New vignette added which gives an overview of regression modelling
using I-priors.
v0.6.5
- Updated documentation.
- Edit FBM kernel. Corrected a mistake. Initially for multivariate
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
.
- Added support for Gaussian process regression with the currently
available kernels.
- Fixed memory leak in FBM kernel function. Also made Canonical kernel
function more efficient.
- While linear I-prior models can perform classification tasks, one
cannot obtain estimation of probabilities for the classes. This is the
motivation behind the [
iprobit
]
(https://github.com/haziqjamil/iprobit) package. By using a probit link,
the I-prior methodology is extended to categorical responses.
- Most functions written here can be used by I-prior probit models in
the
iprobit
package. Added support for categorical response
kernel loading.
- Exported some helper functions like
is.ipriorKernel()
and is.ipriorMod()
.
v0.6.4
- Fixed “override warning” bug in kernel loader when multiple Hurst
coefficients used.
- Updated documentation for
iprior()
and
kernL()
.
- Trimmed down the size of
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()
.
- Fixed a bug with
ipriorOptim()
or
fbmOptim()
whereby standard errors could not be
calculated.
- Added new features to
fbmOptim()
: Ability to specify an
interval to search for, and also the maximum number of iterations for
the initial EM step.
v0.6.3
- Changed some code to match JSS paper.
- Commented on the line where Pearson kernels are always used for
factor-type variables. Should this always be the case?
- Added control option to set intercept at a fixed value.
- Added (hidden) options for
str()
when printing
ipriorKernel
objects.
- Added
fbmOptim()
function to find optimum Hurst
coefficient for fitting FBM I-prior models.
- Added new way to specify Hurst coefficient using the syntax
kernel = "FBM,<value>"
.
- Wrote vignette manual guide which details how to calculate the
matrices required for the closed form estimate of
lambda
.
- Removed the T2 statistic from the
summary()
output for
now.
v0.6.2
- Fix for the installation error (#26) on old R releases (prior to
3.3.0). This error was caused by the generic S3 method
sigma()
not being available from the stats
package prior to R v3.3.0.
v0.6.1
- Several bug fixes and cleanups makes this a CRAN-ready release.
v0.6
- Added documentation for the package.
v0.5.1
- Added multi-stage model fitting via
kernL()
.
v0.5
- Massive improvement to the EM engine which brings about speed
improvements.
- Added a plotting feature.
v0.4.7
v0.4.6
- Added support for Fractional Brownian Motion kernel (i.e. smoothing
models).
v0.4.5
- Added the ‘predicted log-likelihood feature’ in the EM
reporting.
- WARNING: The I-prior package is currently not optimised for large
datasets yet. You might encounter debilitating slowness for
n > 1000
. This is mainly due to the matrix
multiplication and data storing process when the EM initialises. See
issue #20.
v0.4.4
v0.4.3
- Fixed an error in the
predict()
functionality.
v0.4.2
- Added progress feedback reporting feature for the EM algorithm.
v0.4.1
- Improved Pearson kernel generation, but still requires
tweaking.
v0.4
- Added support for Pearson kernels (i.e. regression with categorical
variables)
v0.3
v0.2
- Multiple scale parameters supported.
v0.1
- First useful release.
- Only centred canonical kernel and a single scale parameter able to
be used.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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