Package: spANOVA 0.99.4
spANOVA: Analysis of Field Trials with Geostatistics & Spatial AR Models
Perform analysis of variance when the experimental units are spatially correlated. There are two methods to deal with spatial dependence: Spatial autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019) <doi:10.28951/rbb.v37i2.388>) and geostatistics (see Pontes, J. M., & Oliveira, M. S. D. (2004) <doi:10.1590/S1413-70542004000100018>). For both methods, there are three multicomparison procedure available: Tukey, multivariate T, and Scott-Knott.
Authors:
spANOVA_0.99.4.tar.gz
spANOVA_0.99.4.zip(r-4.5)spANOVA_0.99.4.zip(r-4.4)spANOVA_0.99.4.zip(r-4.3)
spANOVA_0.99.4.tgz(r-4.4-any)spANOVA_0.99.4.tgz(r-4.3-any)
spANOVA_0.99.4.tar.gz(r-4.5-noble)spANOVA_0.99.4.tar.gz(r-4.4-noble)
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spANOVA.pdf |spANOVA.html✨
spANOVA/json (API)
NEWS
| # Install 'spANOVA' in R: |
| install.packages('spANOVA', repos = c('https://lrcastro.r-universe.dev', 'https://cloud.r-project.org')) |
- crd_simulated - Simulated data set for CRD
- rcbd_simulated - Simulated data set for RCBD
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 months agofrom:a97859e774. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | OK | Oct 31 2024 |
| R-4.5-linux | OK | Oct 31 2024 |
| R-4.4-win | OK | Oct 31 2024 |
| R-4.4-mac | OK | Oct 31 2024 |
| R-4.3-win | OK | Oct 31 2024 |
| R-4.3-mac | OK | Oct 31 2024 |
Exports:aovGeoaovSar.crdaovSar.genaovSar.rcbdcontr.tukspANOVAappspCrossvalidspMVTspScottKnottspTukeyspVariofitspVariog
Dependencies:abindapebackportsbase64encbootbroombslibcachemcarcarDataclassclassIntclicodacodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkDBIdeldirDerivdigestdoBydplyrDTe1071evaluatefansifarverfastmapfontawesomeFormulafsgenericsgeoRggplot2gluegtablegtoolshighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalLearnBayeslifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpromisesproxypurrrquantregR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdowns2sandwichsassscalesScottKnottsfshinyshinyBSshinycssloadersshinythemessourcetoolsspSparseMspatialregspDataspdepsplancsstringistringrsurvivalTH.datatibbletidyrtidyselecttinytexunitsutf8vctrsviridisLitewithrwkxfunxtableyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Analysis of variance using a geostatistical model to handle spatial dependence | aovGeo aovGeo.spVariofitRCBD |
| Using a SAR model to handle spatial dependence in a Completely Randomized Design | aovSar.crd |
| Using a SAR model to handle spatial dependence in an aov model. | aovSar.gen |
| Using a SAR model to handle spatial dependence in a Randomized Complete Block Design | aovSar.rcbd |
| Tukey's contrast matrix | contr.tuk |
| Simulated data set for CRD | crd_simulated |
| Simulated data set for RCBD | rcbd_simulated |
| Shiny app for spANOVA | spANOVAapp |
| Cross-validation by kriging | spCrossvalid |
| Multiple comparison test based on multivariate t student distribution | spMVT spMVT.GEOanova spMVT.SARanova |
| The Scott-Knott Clustering Algorithm | spScottKnott spScottKnott.GEOanova spScottKnott.SARanova |
| Compute Tukey Honest Significant Differences for a Spatially Correlated Model | spTukey spTukey.GEOanova spTukey.SARanova |
| Fit a variogram model | spVariofit |
| Compute empirical residual variogram for CRD or RCBD. | spVariog |
