R: Time Series Regression Models with Distributed Lag Models

# Time Series Regression Models with Distributed Lag Models

## Documentation for package ‘dLagM’ version 1.1.13

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

dLagM-package | Implementation of Time Series Regression Models with Distributed Lag Models
ardlBound | Implement ARDL bounds test
ardlBoundOrders | Find optimal orders (lag structure) for ARDL bounds test
ardlDlm | Implement finite autoregressive distributed lag model
dlm | Implement finite distributed lag model
finiteDLMauto | Find the optimal lag length for finite DLMs
forecast | Compute forecasts for distributed lag models
GMRAE | Compute goodness-of-fit measures for DLMs
GoF | Compute goodness-of-fit measures for DLMs
grainProduction | World oats, corn, rice, wheat production, CO2 emissions, temperature anomalies, cropland data
koyckDlm | Implement distributed lag models with Koyck transformation
MAPE | Compute goodness-of-fit measures for DLMs
MASE | Compute goodness-of-fit measures for DLMs
MBRAE | Compute goodness-of-fit measures for DLMs
MRAE | Compute goodness-of-fit measures for DLMs
polyDlm | Implement finite polynomial distributed lag model
rolCorPlot | PLot the rolling correlations
sdPercentiles | Test the significance of signal from rolling correlation analysis
seaLevelTempSOI | Global mean sea level (GMSL), mean land and ocean temperature anomalies, and Southern Oscillation Index (SOI) data
sMAPE | Compute goodness-of-fit measures for DLMs
sortScore | Sort AIC, BIC, MASE, MAPE, sMAPE, MRAE, GMRAE, or MBRAE scores
sunspotTemp | Sunspot numbers and mean temperature anomalies data
warming | Global warming and vehicle production data
wheat | World wheat production, CO2 emissions, and temperature anomalies data