Package: sim2Dpredictr 0.1.1

sim2Dpredictr: Simulate Outcomes Using Spatially Dependent Design Matrices

Provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288> and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user.

Authors:Justin Leach [aut, cre, cph]

sim2Dpredictr_0.1.1.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
sim2Dpredictr/json (API)
NEWS

# Install 'sim2Dpredictr' in R:

install.packages('sim2Dpredictr', repos = c('https://jmleach-bst.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jmleach-bst/sim2dpredictr/issues

On CRAN:

2.70 score 2 scripts 136 downloads 18 exports 22 dependencies

Last updated from:339a4aef8e. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	139
source / vignettes	OK	185
linux-release-x86_64	NOTE	136
macos-release-arm64	NOTE	131
macos-oldrel-arm64	NOTE	155
windows-devel	NOTE	80
windows-release	NOTE	92
windows-oldrel	NOTE	96
wasm-release	OK	101

Exports:beta_builder chol_s2Dp classify_multiclass corr_fun correlation_builder generate_grid generate_multinom_probs inf_2D_image neighbors_by_dist precision_builder proximity_builder sample_FP_Power sim_MVN_X sim_Y_Binary_X sim_Y_MVN_X sim2D_binarymap sim2D_RandSet_HPPP within_area

Dependencies:cli dotCall64 dplyr generics glue lifecycle magrittr MASS matrixcalc pillar pkgconfig R6 rbibutils Rcpp Rdpack rlang spam tibble tidyselect utf8 vctrs withr

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

Package: sim2Dpredictr 0.1.1

sim2Dpredictr: Simulate Outcomes Using Spatially Dependent Design Matrices

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