Package: SPIChanges 0.3.0

Gabriel Constantino Blain

SPIChanges: Improves the Interpretation of the Standardized Precipitation Index Under Changing Climate Conditions

Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) <doi:10.1002/joc.7550> to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring.

Authors:Gabriel Constantino Blain [aut, cre], Graciela R. Sobierajski [aut], Leticia L. Martins [aut], Adam H. Sparks [aut]

SPIChanges_0.3.0.tar.gz
SPIChanges_0.3.0.zip(r-4.7-any)SPIChanges_0.3.0.zip(r-4.6-any)SPIChanges_0.3.0.zip(r-4.5-any)
SPIChanges_0.3.0.tgz(r-4.6-any)SPIChanges_0.3.0.tgz(r-4.5-any)
SPIChanges_0.3.0.tar.gz(r-4.7-any)SPIChanges_0.3.0.tar.gz(r-4.6-any)
SPIChanges_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SPIChanges/json (API)

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

Bug tracker:https://github.com/gabrielblain/spichanges/issues

Datasets:
  • CampinasRain - Daily Rainfall Amounts
  • lonlat - Coordinates for Entire Brazil
  • Map - Changes in Drought Events in Brazil
  • OxfordRain - Daily Rainfall Amounts
  • rainTS4 - Rainfall Aggregated at the 4-quasi week time scale

On CRAN:

Conda:

5.30 score 5 stars 8 scripts 430 downloads 2 exports 16 dependencies

Last updated from:d3c5ad471e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK163
source / vignettesOK256
linux-releaseOK161
macos-releaseOK136
macos-oldrelOK105
windows-develOK126
windows-releaseOK130
windows-oldrelOK129
wasm-releaseOK103

Exports:SPIChangesTSaggreg

Dependencies:brglm2cpp11dglmenrichwithgenericslatticelubridateMASSMatrixnleqslvnnetnumDerivrlangstatmodtimechangezoo

SPIChanges package: Monte Carlo Experiments and Case Studies
Case Studies Applications | Case Study 1 -- Oxford Rainfall | Analyse Data Looking for Changes | Interpreting the Output Data Fields from the “Oxford.Changes” | $model.selection | $Statistics | Key Observations: | $Changes.Freq.Drought | Summary: | Interpreting the Output Data Fields from the “Oxford.Changes.linear” | $Statistics and $Changes.Freq.Drought | Case Study 2 - Brazil Drought Events | Aggregating daily data into a specified time scale: | Applying the SPIChanges::SPIChanges() for entire Brazil | Assessing changes in severe to moderate drought events | References
Rendered fromCaseStudies.Rmdusingknitr::rmarkdown

Last update: 2026-09-11
Started: 2024-12-15

SPIChanges
Introduction | Getting Started | Data | Function TSaggreg() | Arguments | Value | Example 1 | Function SPIChanges() | Usage | Example 2 | Example 3
Rendered fromSPIChanges.Rmdusingknitr::rmarkdown

Last update: 2025-02-03
Started: 2024-11-26