Package: remotePARTS 1.0.4

Clay Morrow

remotePARTS: Spatiotemporal Autoregression Analyses for Large Data Sets

These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.

Authors:Clay Morrow [aut, cre], Anthony Ives [aut]

remotePARTS_1.0.4.tar.gz
remotePARTS_1.0.4.tar.gz(r-4.5-noble)remotePARTS_1.0.4.tar.gz(r-4.4-noble)
remotePARTS_1.0.4.tgz(r-4.4-emscripten)remotePARTS_1.0.4.tgz(r-4.3-emscripten)
remotePARTS.pdf |remotePARTS.html✨
remotePARTS/json (API)
NEWS

# Install 'remotePARTS' in R:
install.packages('remotePARTS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/morrowcj/remoteparts/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • ndvi_AK10000 - NDVI remote sensing data for 10,000 random pixels from Alaska, with rare land classes removed.
  • partGLS_ndviAK - Partitioned GLS results

cppopenmp

2.70 score 16 scripts 200 downloads 20 exports 10 dependencies

Last updated 1 years agofrom:aa1aa997c7. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 10 2024
R-4.5-linux-x86_64OKDec 10 2024

Exports:check_posdefchisqrcovar_expcovar_exppowcovar_taperdistm_kmdistm_scaledfitARfitAR_mapfitCLSfitCLS_mapfitCorfitGLSfitGLS_optfitGLS_partitioninvert_cholmulticore_fitGLS_partitionpart_csvpart_datasample_partitions

Dependencies:codetoolsCompQuadFormdoParallelforeachgeosphereiteratorslatticeRcppRcppEigensp

Alaska

Clay Morrow

Rendered fromAlaska.Rmdusingknitr::rmarkdownon Dec 10 2024.

Last update: 2023-09-15
Started: 2023-09-15

Readme and manuals

Help Manual

Help pageTopics
calculate degrees of freedom for partitioned GLScalc_dfpart
Check if a matrix is positive definitecheck_posdef
Conduct a chi-squared testchisqr
Conduct a chisqr test of "partGLS" objectchisqr.partGLS
Tapered-spherical distance-based covariance functioncovar_exp covar_exppow covar_taper
Calculate cross-partition statistics in a partitioned GLScrosspart_GLS
Calculate a distance matrix from coordinatesdistm_km distm_scaled
AR regressions by REMLAR_fun fitAR
Map-level AR REMLfitAR_map
CLS for time seriesfitCLS
Map-level CLS for time seriesfitCLS_map
Estimate spatial parameters from time series residualsfitCor
Fit a PARTS GLS model.fitGLS
Fit a PARTS GLS model, with maximum likelihood spatial parametersfitGLS_opt
Function that fitGLS_opt optimizes overfitGLS_opt_FUN
Invert the cholesky decomposition of Vinvert_chol
calculate maximum distance among a table of coordinatesmax_dist
fit a parallel partitioned GLSfitGLS_partition MCGLS_partsummary MC_GLSpart multicore_fitGLS_partition part_csv part_data
NDVI remote sensing data for 10,000 random pixels from Alaska, with rare land classes removed.ndvi_AK10000
Find the maximum likelihood estimate of the nuggetoptimize_nugget
Chisqr test for partitioned GLSpart_chisqr
Correlated t-test for paritioned GLSpart_ttest
partitioned GLS resultspartGLS_ndviAK
S3 print method for "partGLS" objectsprint.partGLS
S3 print method for "remoteCor" classprint.remoteCor
print method for remoteGLSprint.remoteGLS
S3 print method for remoteTS classprint.mapTS print.remoteTS smry_funM smry_funV summary.mapTS summary.remoteTS
remoteGLS constructor (S3)remoteGLS
Randomly sample a partition matrix for partitioned GLSsample_partitions
Conduct a t-test of "partGLS" objectt.test.partGLS
Test passing a covariance function and argumentstest_covar_fun