dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty

The dubicube package aims to deliver measures for assessing the applicability of biodiversity data cubes, whether for general use or specific biodiversity indicators. These measures facilitate data exploration by providing insights into data quality and reliability. Additionally, the package includes functions for calculating indicator uncertainty using bootstrapping, as well as tools for interpreting and visualising uncertainty in biodiversity indicators derived from occurrence cubes.

Installation

Install dubicube in R:

install.packages("dubicube", repos = c("https://b-cubed-eu.r-universe.dev", "https://cloud.r-project.org"))

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("b-cubed-eu/dubicube")

Key Features

The dubicube package offers:

🔍 1. Data Exploration & Variability Assessment

Gain insights into the structure and sensitivity of biodiversity data cubes.

• cross_validate_cube()
  Perform cross-validation (leave-one-out or k-fold) to assess group-level sensitivity of indicators and evaluate how individual categories influence results.
  📘 Read the tutorial →

🛠️ Additional data quality diagnostics are on the way!

📈 2. Estimating Indicator Uncertainty

Use bootstrap methods to understand variability, bias, and confidence in your indicators.

• bootstrap_cube()
  Create bootstrap replicates to estimate indicator variability, bias, and standard error.
  📘 Read the tutorial →

• calculate_bootstrap_ci()
  Compute confidence intervals (percentile, BCa, normal, basic), with optional transformations and bias correction.
  📘 Read the tutorial →

🧠 3. Interpretation & Visualisation

Put your results in context with reference values and uncertainty thresholds.

• add_effect_classification()
  Classify indicator trends (e.g. increase, stable, decrease) by comparing confidence intervals to thresholds.
  📘 Read the tutorial →

• Spatial and temporal interpretation
  Learn how to visualise and assess patterns across space and time using indicator uncertainty.
  📘 Best practices for temporal trends →
  📘 Best practices for spatial trends →

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🔗 Learn more at our website or explore the documentation.