Theta conditional plus cleanup

.Rbuildignore

@@ -2,4 +2,8 @@
 ^\.Rproj\.user$
 ^figures
 ^data
-^simulations
+^simulations
+^application
+^other_code
+^testing
+^\.vscode

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: spiox
 Type: Package
-Title: Inside-Out Cross-covariance for spatial multivariate data
-Version: 0.3
-Date: 2024-10-11
+Title: Inside-out cross-covariance (IOX) for spatial multivariate data
+Version: 0.5
+Date: 2024-12-02
 Author: Michele Peruzzi
 Maintainer: Michele Peruzzi <peruzzi@umich.edu>
-Description: Fits Bayesian models for multivariate spatial data using Inside-Out Cross-covariance and Vecchia approximations of GPs.
+Description: Fits Bayesian models for multivariate spatial data using IOX and Vecchia approximations of GPs.
 License: GPL (>= 2)
 Encoding: UTF-8
 RoxygenNote: 7.3.1

R/RcppExports.R

@@ -57,19 +57,23 @@ run_spf_model <- function(Y, n_factors, delta_gamma_shape, delta_gamma_rate, dl_
     .Call(`_spiox_run_spf_model`, Y, n_factors, delta_gamma_shape, delta_gamma_rate, dl_dirichlet_a, Lambda_start, Delta_start, mcmc, print_every, seq_lambda)
 }
 
-spiox_wishart <- function(Y, X, coords, custom_dag, theta_opts, Sigma_start, mvreg_B_start, mcmc = 1000L, print_every = 100L, matern = TRUE, sample_iwish = TRUE, sample_mvr = TRUE, sample_theta_gibbs = TRUE, upd_theta_opts = TRUE, num_threads = 1L) {
+spiox_wishart <- function(Y, X, coords, custom_dag, theta_opts, Sigma_start, mvreg_B_start, mcmc = 1000L, print_every = 100L, matern = 1L, sample_iwish = TRUE, sample_mvr = TRUE, sample_theta_gibbs = TRUE, upd_theta_opts = TRUE, num_threads = 1L) {
     .Call(`_spiox_spiox_wishart`, Y, X, coords, custom_dag, theta_opts, Sigma_start, mvreg_B_start, mcmc, print_every, matern, sample_iwish, sample_mvr, sample_theta_gibbs, upd_theta_opts, num_threads)
 }
 
-spiox_latent <- function(Y, X, coords, custom_dag, theta_opts, Sigma_start, mvreg_B_start, mcmc = 1000L, print_every = 100L, matern = TRUE, sample_iwish = TRUE, sample_mvr = TRUE, sample_theta_gibbs = TRUE, upd_theta_opts = TRUE, num_threads = 1L, sampling = 2L) {
+spiox_latent <- function(Y, X, coords, custom_dag, theta_opts, Sigma_start, mvreg_B_start, mcmc = 1000L, print_every = 100L, matern = 1L, sample_iwish = TRUE, sample_mvr = TRUE, sample_theta_gibbs = TRUE, upd_theta_opts = TRUE, num_threads = 1L, sampling = 2L) {
     .Call(`_spiox_spiox_latent`, Y, X, coords, custom_dag, theta_opts, Sigma_start, mvreg_B_start, mcmc, print_every, matern, sample_iwish, sample_mvr, sample_theta_gibbs, upd_theta_opts, num_threads, sampling)
 }
 
-spiox_predict <- function(X_new, coords_new, Y, X, coords, dag, B, Sigma, theta, matern = TRUE, num_threads = 1L) {
+spiox_predict <- function(X_new, coords_new, Y, X, coords, dag, B, Sigma, theta, matern = 1L, num_threads = 1L) {
     .Call(`_spiox_spiox_predict`, X_new, coords_new, Y, X, coords, dag, B, Sigma, theta, matern, num_threads)
 }
 
-spiox_latent_predict <- function(X_new, coords_new, coords, dag, W, B, Sigma, Dvec, theta, matern = TRUE, num_threads = 1L) {
+spiox_predict_part <- function(Y_new, X_new, coords_new, Y, X, coords, dag, B, Sigma, theta, matern = 1L, num_threads = 1L) {
+    .Call(`_spiox_spiox_predict_part`, Y_new, X_new, coords_new, Y, X, coords, dag, B, Sigma, theta, matern, num_threads)
+}
+
+spiox_latent_predict <- function(X_new, coords_new, coords, dag, W, B, Sigma, Dvec, theta, matern = 1L, num_threads = 1L) {
     .Call(`_spiox_spiox_latent_predict`, X_new, coords_new, coords, dag, W, B, Sigma, Dvec, theta, matern, num_threads)
 }
 

README.md

@@ -1,11 +1,11 @@
-# Inside-Out Cross-covariance for multivariate Gaussian processes
+# Inside-out cross-covariance for multivariate Gaussian processes
 
-![Simulated data from IOX](figures/prior_sample_3.png)
+![Simulated data from IOX](https://github.com/mkln/spiox-paper/figures/prior_sample_3.png)
 
 Noise-free trivariate GP using IOX.
 
 
-![Cross-covariance](figures/cij_plot.png)
+![Cross-covariance](https://github.com/mkln/spiox-paper/figures/cij_plot.png)
 
 Cross-covariance between two variables, for varying spatial range (left) or smoothness (right) of one of them relative to the other.