Simulate Outcomes Using Spatially Dependent Design Matrices

Documentation for package ‘sim2Dpredictr’ version 0.1.1

beta_builder	Create a Parameter Vector from Lattice Locations
chol_s2Dp	Build and Take the Cholesky Decomposition of a Covariance Matrix
classify_multiclass	Classify subjects based on predicted probabilities for each class
correlation_builder	Build a Correlation Matrix for 2D Spatial Data
corr_fun	Specify the Correlation Function between Two Locations
generate_grid	Convert a 2D Space to Grid Coordinates
generate_multinom_probs	Generate Probabilities for Multinomial Draws
inf_2D_image	Display Inference Results for 2D Predictors
make_rejection	Determine rejections
neighbors_by_dist	Determine and store neighbors by Euclidean Distance Constraints
precision_builder	Construct a Precision Matrix
proximity_builder	Generate a Proximity Matrix
sample_FP_Power	Obtain Sample False Positive Rates and Power
sim2D_binarymap	Generate a Binary Map via the Boolean Method
sim2D_RandSet_HPPP	Generate a Random Set Using a Poisson Process and Random Radii About Events
sim_MVN_X	Simulate Spatially Correlated MVN Data
sim_Y_Binary_X	Simulate Scalar Outcomes from Simulated Spatially Dependent Binary Predictors
sim_Y_MVN_X	Simulate Scalar Outcomes from Simulated Spatially Correlated Predictors
within_area	Determine Whether Lattice Points are Within or Without a Random Set