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().