Package: SPmodels 0.1.1
Olatunji Johnson
SPmodels: Spatial Models for Research and Teaching
Independently implemented spatial modelling methods with a formula-oriented interface, transparent uncertainty, and mathematical derivations for research and teaching. The documented course workflows have regression coverage. This package has not been submitted to CRAN.
Authors:
SPmodels_0.1.1.tar.gz
SPmodels_0.1.1.zip(r-4.7-any)SPmodels_0.1.1.zip(r-4.6-any)SPmodels_0.1.1.zip(r-4.5-any)
SPmodels_0.1.1.tgz(r-4.6-any)SPmodels_0.1.1.tgz(r-4.5-any)
SPmodels_0.1.1.tar.gz(r-4.7-any)SPmodels_0.1.1.tar.gz(r-4.6-any)
SPmodels_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SPmodels/json (API)
| # Install 'SPmodels' in R: |
| install.packages('SPmodels', repos = c('https://olatunjijohnson.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/olatunjijohnson/spmodels/issues
Last updated from:47bbe4a8c3 (on v0.1.1). Checks:4 ERROR, 5 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | ERROR | 107 | ||
| source / vignettes | OK | 177 | ||
| linux-release | ERROR | 111 | ||
| macos-release | ERROR | 113 | ||
| macos-oldrel | ERROR | 106 | ||
| windows-devel | OK | 114 | ||
| windows-release | OK | 75 | ||
| windows-oldrel | OK | 97 | ||
| wasm-release | OK | 65 |
Exports:matern_cormatern_scale_derivativesp_block_foldssp_buffered_foldssp_calibrationsp_controlsp_cross_validatesp_diagnosticssp_distance_summarysp_environmental_foldssp_fitsp_fit_neighborsp_format_tablesp_gridsp_group_foldssp_interventionsp_intervention_curvesp_intervention_multipliersp_latent_modesp_latent_samplesp_mcmc_controlsp_mcmlsp_mcml_controlsp_mcml_precisionsp_mcml_replicatessp_model_tablesp_neighbor_covariancesp_nonstationary_corsp_polygon_area_weightssp_polygon_weightssp_predict_chunkedsp_predict_drawssp_predict_draws_neighborsp_predict_targetsp_predict_target_neighborsp_prediction_tablesp_prediction_updatesp_predictive_log_scoresp_random_slopesp_reportsp_sampler_diagnosticssp_simulation_assesssp_simulation_gridsp_simulation_precisionsp_simulation_recoverysp_simulation_scenariossp_spatial_foldssp_target_geometrysp_temporalsp_utm_crssp_validation_tablesp_variogramsp_variogram_envelopespacetime_cor
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Convert a prediction-target result to its summary table | as.data.frame.spmodels_prediction_target |
| Confidence Intervals for Fitted SPmodels Parameters | confint.spmodels_fit |
| Matérn Correlation | matern_cor |
| Matérn Correlation Derivatives with Respect to Scale | matern_scale_derivative |
| Display validation and simulation results | plot.spmodels_cross_validation plot.spmodels_simulation_assessment plot.spmodels_simulation_recovery |
| Predict from a Fitted SPmodels Model | predict.spmodels_fit |
| Simulate from a Fitted Spatial Model | simulate.spmodels_fit |
| Construct Regular Spatial-Block Folds | print.spmodels_block_folds sp_block_folds |
| Construct Buffered Spatial Validation Folds | print.spmodels_buffered_folds sp_buffered_folds |
| Assess and display predictive interval calibration | plot.spmodels_calibration print.spmodels_calibration sp_calibration |
| Control Gaussian Spatial Model Fitting | sp_control |
| Fixed-Fold Cross-Validation for an SPmodels Fit | print.spmodels_cross_validation sp_cross_validate |
| Fitted Spatial-Model Diagnostics | plot.spmodels_diagnostics sp_diagnostics |
| Summarise Pairwise Spatial Distances | print.spmodels_distance_summary sp_distance_summary |
| Construct Environmental-Space Validation Folds | print.spmodels_environmental_folds sp_environmental_folds |
| Fit a Geostatistical Regression | coef.spmodels_fit fitted.spmodels_fit logLik.spmodels_fit print.spmodels_fit print.summary.spmodels_fit residuals.spmodels_fit sp_fit summary.spmodels_fit vcov.spmodels_fit |
| Fit an approximate Gaussian nearest-predecessor model | coef.spmodels_neighbor_fit confint.spmodels_neighbor_fit fitted.spmodels_neighbor_fit logLik.spmodels_neighbor_fit predict.spmodels_neighbor_fit print.spmodels_neighbor_fit print.summary.spmodels_neighbor_fit residuals.spmodels_neighbor_fit sp_fit_neighbor summary.spmodels_neighbor_fit vcov.spmodels_neighbor_fit |
| Format a stable SPmodels table for presentation | print.spmodels_formatted_table sp_format_table |
| Construct a Regular Prediction Grid | sp_grid |
| Construct Group-Constrained Validation Folds | print.spmodels_group_folds sp_group_folds |
| Specify a Decay-Adjusted Intervention History | print.spmodels_intervention sp_intervention |
| Summarise a Decay-Adjusted Intervention Effect over Time | plot.spmodels_intervention_curve print.spmodels_intervention_curve sp_intervention_curve |
| Calculate Decay-Adjusted Intervention Multipliers | sp_intervention_multiplier |
| Calculate a Latent Gaussian Mode | sp_latent_mode |
| Sample a Non-Gaussian Latent Spatial Vector | sp_latent_sample |
| Control Latent MCMC Sampling | sp_mcmc_control |
| Refine a Non-Gaussian Fit by Monte Carlo Maximum Likelihood | sp_mcml |
| Control Monte Carlo Maximum-Likelihood Correction | print.spmodels_mcml_control sp_mcml_control |
| Escalate MCML sample size to declared precision targets | sp_mcml_precision |
| Repeat Monte Carlo maximum-likelihood fits independently | print.spmodels_mcml_replicates sp_mcml_replicates |
| Structured model-estimate table | sp_model_table |
| Inspect Nearest-Predecessor Predictive Covariance | print.spmodels_neighbor_covariance sp_neighbor_covariance |
| Non-stationary Matérn correlation | sp_nonstationary_cor |
| Construct Exact Fractional Cell-Area Polygon Weights | sp_polygon_area_weights |
| Assign Point Prediction Weights to Polygon Regions | sp_polygon_weights |
| Predict a Large Point Set in Bounded-Memory Chunks | sp_predict_chunked |
| Joint Predictive Draws from a Fitted Spatial Model | print.spmodels_prediction_draws sp_predict_draws |
| Bounded-Memory Approximate Joint Predictive Draws | sp_predict_draws_neighbor |
| Summarise Targets from Joint Predictive Draws | print.spmodels_prediction_target sp_predict_target |
| Stream Bounded-Memory Approximate Predictive Targets | sp_predict_target_neighbor |
| Structured prediction-summary table | sp_prediction_table |
| Compare predictions under an updated predictor scenario | print.spmodels_prediction_update sp_prediction_update |
| Integrated Pointwise Predictive Log Scores | sp_predictive_log_score |
| Specify a Grouped Random Slope | print.spmodels_random_slope sp_random_slope |
| Assemble an SPmodels analysis report | print.spmodels_report sp_report |
| Diagnose Retained Latent Samples | plot.spmodels_sampler_diagnostics print.spmodels_sampler_diagnostics sp_sampler_diagnostics |
| Assess Predictive Draws Against Known Values | print.spmodels_simulation_assessment sp_simulation_assess |
| Construct a Cartesian Grid of Simulation-Recovery Scenarios | sp_simulation_grid |
| Precision-Directed Simulation Recovery | print.spmodels_simulation_precision sp_simulation_precision |
| Repeated Fitted-Model Simulation and Parameter Recovery | print.spmodels_simulation_recovery sp_simulation_recovery |
| Run Parameter-Recovery Experiments Across Fitted Scenarios | print.spmodels_simulation_scenarios sp_simulation_scenarios |
| Construct Geometry-Driven Spatial Folds | print.spmodels_spatial_folds sp_spatial_folds |
| Attach Prediction-Target Summaries to Spatial Geometry | plot.spmodels_target_sf sp_target_geometry |
| Specify Survey Time for a Separable Space-Time Model | print.spmodels_temporal sp_temporal |
| Choose a Projected UTM Coordinate Reference System | print.spmodels_crs_choice sp_utm_crs |
| Structured validation table | sp_validation_table |
| Empirical Residual Variograms Before and After Spatial Fitting | plot.spmodels_variogram sp_variogram |
| Monte Carlo Envelope for a Residual Variogram | plot.spmodels_variogram_envelope print.spmodels_variogram_envelope sp_variogram_envelope |
| Separable Matern-Exponential Space-Time Correlation | spacetime_cor |