Saylac

Saylac is an R/Shiny application for multidisciplinary spatial, longitudinal, time-series, forecasting, and diagnostic analysis of global, national, and regional indicators.

SAYLAC abbreviates Spatial Analysis of Yearly, Longitudinal, and Areal Change.

The package keeps the earlier SAW-SIMODI-SURAD analytical engine but presents it through a clearer and more memorable CRAN-facing name. It can be used for indicators from education, health, poverty, economy, environment, demography, infrastructure, governance, and other development fields.

Main features

• Spatial diagnostics for country-level and regional indicators.
• Longitudinal exploration of indicators observed across years.
• Time-series diagnostics using stationarity and trend tests.
• Comparative forecasting using ARIMA, ETS, BATS, TBATS, Theta, NNAR, MLP, and random-walk drift models.
• Spatial mapping of forecast outputs.
• Automated HTML, Word, and PDF reporting.
• Example data for quick testing.

Published application

The live application is available at:

https://muse252.shinyapps.io/Saylac_Shiny_App_Ready/

Installation

After CRAN acceptance, install with:

install.packages("Saylac")

For GitHub development installation, use the repository once it is public:

remotes::install_github("Abdisalammuse/Saylac", dependencies = TRUE)

Launch the app

library(Saylac)
run_saylac()

Backward-compatible launch command:

run_saw_simodi_surad()

Example data

saylac_example_data()

The app accepts country-year data in CSV format. A common structure is:

Country,Year,Value
Kenya,2020,7.9
Uganda,2020,6.1

Applied reference

The platform was first applied in:

Touryare, M. S. M., & Mohamud, M. A. (2026). Mapping the path to SDG 4 through integrated spatiotemporal forecasting of educational attainment in Eastern Africa from 1990 to 2030. Discover Sustainability. https://doi.org/10.1007/s43621-026-04022-x

Citation

citation("Saylac")

License

GPL (>= 3)