- Modification to centering of SE and polynomial kernels.
- Added option
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.
- 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
- 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
- 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
- The legacy kernels
Pearson are now referred to as
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:
- Updated documentation throughout.
- New vignette added which gives an overview of regression modelling using I-priors.
- Updated documentation.
- Edit FBM kernel. Corrected a mistake. Initially for multivariate
H(x) = H1(x) + ... + H_p(x[p]). This is only true for Canonical kernel. Now correctly applies the FBM kernel using the norm function on each multivariate
- 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
- Fixed “override warning” bug in kernel loader when multiple Hurst coefficients used.
- Updated documentation for
- Trimmed down the size of
ipriorMod objects by not saving
VarY.inv. Although these are no longer stored within an
ipriorMod object, they can still be retrieved via the functions
- Fixed a bug with
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.
- 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
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
- Removed the T2 statistic from the
summary() output for now.
- 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.
- Several bug fixes and cleanups makes this a CRAN-ready release.
- Added documentation for the package.
- Added multi-stage model fitting via
- Massive improvement to the EM engine which brings about speed improvements.
- Added a plotting feature.
- Added support for Fractional Brownian Motion kernel (i.e. smoothing models).
- 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.
- Fixed an error in the
- Added progress feedback reporting feature for the EM algorithm.
- Improved Pearson kernel generation, but still requires tweaking.
- Added support for Pearson kernels (i.e. regression with categorical variables)
- Multiple scale parameters supported.
- First useful release.
- Only centred canonical kernel and a single scale parameter able to be used.