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Using R for ecological modeling in USACE

Table of contents

  • Using R for ecological modeling in USACE restoration planning
  • Required set-up for the course
  • Basic Training on R and RStudio
  • 1 Introduction to R and RStudio
  • 2 Data Visualization with ggplot2
  • 3 Exploring and understanding data
  • 4 Manipulating Tabular Data
  • Ecological Modeling in R
  • 5 Conceptual Ecological Models
  • Habitat Models
  • 6 Background on habitat models in USACE
  • 7 Habitat Suitability Index models with ecorest
  • 8 Ecorest Web App
  • 9 Sensitivity and uncertainty analysis for habitat suitability index (HSI) models
  • 10 Spatial Habitat Models
  • Linear Models for informing planning HSIs
  • 11 Linear Models
  • 12 Generalized Linear Models
  • 13 Random Effects
  • 14 Generalized Additive Models
  • Machine Learning
  • 15 Random Forest
  • 16 Boosted Regression Trees
  • Other Modeling Approaches
  • 17 Population Modeling
  • 18 Network Models and Connectivity
  • 19 Agent-based Models
  • Models and decision-making
  • 20 Decision Support
  • 21 Annualizing benefits and costs with ecorest and EngrEcon
  • 22 Cost-Effectiveness and Incremental Cost Analysis (CEICA) with ecorest
  • Models Integration
  • 23 Model Integration
  • Other R applications
  • 24 R for GIS
  • Environmental data sources
  • 25 Using R for data access
  • 26 USGS gage data using the dataRetrieval package
  • 27 National Hydrography Dataset

16 Boosted Regression Trees

Adapted from this Carpentries workshop https://carpentries-incubator.github.io/r-ml-tabular-data/

Boosted trees portion here https://carpentries-incubator.github.io/r-ml-tabular-data/05-Gradient-Boosting/index.html

15 Random Forest
17 Population Modeling

On this page

  • 16 Boosted Regression Trees

"Using R for ecological modeling in USACE" was written by Ed Stowe.

This book was built by the bookdown R package.