R: Simulate Outcomes Using Spatially Dependent Design Matrices

# Simulate Outcomes Using Spatially Dependent Design Matrices

## Documentation for package ‘sim2Dpredictr’ version 0.1.1

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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