ecoXCorr 0.2.2

Bug fixes

• Correct default value for arg shift in ecoXCorr() function (from -1 to 1)

Improvements

• Correct R code output of the shiny app.

ecoXCorr 0.2.1

• Add a warning when providing ecoXCorr() function with multiple time series (i.e. when duplicated dates are found in meteo_data)

ecoXCorr 0.2.0

New features

• aggregate_lagged_intervals() now supports multiple independent time series via a new id_col argument. When specified, lagged aggregations are performed separately within each group, preventing unintended mixing of values across distinct time series (e.g. sites, stations, individuals).

Improvements

• Improved robustness of fit_models_by_lag() when computing R² using performance::r2(). Warnings are now handled more gracefully, and R² is re-evaluated when possible instead of returning NA.
• More informative warning messages are collected and returned to the user when issues occur during R² computation.

Bug fixes

• Fixed aggregation interval: The function aggregate_lagged_intervals() has been corrected to properly include the last day of each interval in the aggregation. Previously, intervals were defined as start <= date < end, which systematically excluded the final day. This has now been changed to start <= date <= end, ensuring that all dates within the intended interval are considered when computing summary statistics. This fix guarantees that the aggregated results accurately reflect the intervals specified by the user. In addition, default for parameter shift has been set to 1, i.e. intervals ends the day preceding ref_date (this latter being excluded).

• Fixed an error occurring in fit_models_by_lag() during result aggregation: Error in match.names(clabs, names(xi)) : names do not match previous names This error occurred when performance::r2() failed (error or warning) for some models, leading to inconsistent column structures across elements of the results list. The function now ensures consistent output structure across all lag windows by:

• standardizing R² extraction,

• returning properly formatted values even when warnings occur,

• preventing failures in do.call(rbind, results).

Notes

• This update improves stability when fitting large numbers of models or using complex model families (e.g. zero-inflated or truncated distributions via glmmTMB).