Package: BRISC 1.0.6

Arkajyoti Saha

BRISC: Fast Inference for Large Spatial Datasets using BRISC

Fits bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest neighbor Gaussian processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.

Authors:Arkajyoti Saha [aut, cre], Abhirup Datta [aut], Jorge Nocedal [ctb], Naoaki Okazaki [ctb], Lukas M. Weber [ctb]

BRISC_1.0.6.tar.gz
BRISC_1.0.6.tar.gz(r-4.5-noble)BRISC_1.0.6.tar.gz(r-4.4-noble)
BRISC_1.0.6.tgz(r-4.4-emscripten)BRISC_1.0.6.tgz(r-4.3-emscripten)
BRISC.pdf |BRISC.html
BRISC/json (API)

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

Peer review:

Bug tracker:https://github.com/arkajyotisaha/brisc/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

openblascppopenmp

2.40 score 2 packages 42 scripts 625 downloads 9 exports 6 dependencies

Last updated 3 months agofrom:f9734a6cbd. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linux-x86_64OKDec 02 2024

Exports:BRISC_bootstrapBRISC_correlationBRISC_decorrelationBRISC_estimationBRISC_neighborBRISC_orderBRISC_predictionBRISC_simulationBRISC_variogram.ci

Dependencies:matrixStatspbapplyRANNRcppRcppArmadillordist