These have STAN_kernel_* counterparts. These R versions are provided for reference and are not optimized for speed. These are used when generating simulated data, and not during model inference.

kernel_eq(x1, x2, alpha = 1, ell)

kernel_ns(x1, x2, alpha = 1, ell, a)

kernel_zerosum(x1, x2, M)

kernel_bin(x1, x2, pos_class = 0)

kernel_cat(x1, x2)

kernel_beta(beta, idx1_expand, idx2_expand)

## Arguments

x1 vector of length $$n$$ vector of length $$m$$ marginal std (default = 1) lengthscale steepness of the warping function rise number of categories binary (mask) kernel function has value one if both inputs have this value, other wise it is zero vector of two mask function parameters. a parameter vector (row vector) of length N_cases integer vector of length $$n$$ integer vector of length $$m$$

## Value

A matrix of size $$n$$ x $$m$$.

## Functions

• kernel_eq: Uses the exponentiated quadratic kernel.

• kernel_ns: Uses the non-stationary kernel (input warping + squared exponential).

• kernel_zerosum: Uses the zero-sum kernel. Here, x1 and x2 must be integer vectors (integers denoting different categories). Returns a binary matrix.

• kernel_bin: Uses the binary (mask) kernel. Here, x1 and x2 must be integer vectors (integers denoting different categories). Returns a binary matrix.

• kernel_cat: Uses the categorical kernel. Here, x1 and x2 must be integer vectors (integers denoting different categories). Returns a binary matrix.

• kernel_varmask: Computes variance mask multiplier matrix. NaN's in x1 and x2 will be replaced by 0.

• kernel_beta: Computes the heterogeneity multiplier matrix. NOTE: idx_expand needs to be given so that idx_expand[j]-1 tells the index of the beta parameter that should be used for the $$j$$th observation. If observation $$j$$ doesn't correspond to any beta parameter, then idx_expand[j] should be 1.