binnednp

binnednp implements nonparametric kernel density and distribution estimators for interval-grouped data to analyze weed emergence and environmental indices such as hydrothermal time (HTT).

Key Features:

• Kernel estimators for interval-grouped data: Implements kernel density and distribution estimation methods adapted to data aggregated into intervals rather than individual observations.
• Bootstrap confidence bands: Computes bootstrap confidence bands for estimated density and distribution functions to quantify estimate uncertainty.
• Comparative bootstrap analyses: Performs comparative analyses across treatments using bootstrap approaches to assess differences between groups.
• Cramér-von Mises type distance: Uses the Cramér-von Mises–type distance to quantify differences between distributions for treatment comparison.
• Bandwidth selection procedures: Provides multiple bandwidth selection methods to optimize kernel estimation for specific data characteristics.
• Emergence indices estimation: Estimates emergence indices that describe distribution shape and assist in determining optimal soil depths for measuring HTT.

Scientific Applications:

• Weed emergence analysis: Analyze temporal distribution and density of weed emergence from interval-grouped experimental data.
• Treatment effect comparison: Compare treatment effects on emergence patterns using distributional comparisons and bootstrap inference.
• Environmental index evaluation: Relate emergence distributions to environmental indices such as hydrothermal time (HTT) and identify optimal soil depths for HTT measurement.

Methodology:

Applies nonparametric kernel estimation adapted for interval-grouped data, bootstrap methods for confidence bands and comparative inference, Cramér-von Mises type distance for distributional comparisons, and multiple bandwidth selection procedures.