Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. Theauthors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications.The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying andusing forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming.The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statisticsand very basic applied mathematics.Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document.Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University.Both authors have presented numerous R workshops to forestry professionals and scientists, and others.

Introduction.- Forest data management.- Data analysis for common inventory methods.- Imputation and Interpolation.- Fitting dimensional distributions.- Linear and non-linear models.- Fitting linear hierarchical models.- Simulations.- Forest estate planning and optimization.