Part 1: species composition - model based analysis

• lapwing.r: various likelihood computations for comparison of lapwing counts in 1930's and 1960's
• lnlNB.r Negative binomial likelihood computations, continuing lapwing.r. Includes using optim() and optimize()
• counts.r Code used to compare Poisson and negative binomial distributions. Totally optional.
• Output and explanation of code in lnlNB.r and some code in lapwing.r
• mvabund.r Code to analyze species composition using a model-based method
• Output and explanation of code in mvabund.r

Part 2: mark recapture

• lnlM0t.r: lnL functions and profile lnL functions for M0 and Mt, profileCI function to compute lower and upper bounds of the profile CI
• redpoll.r: Code to use the functions in lnlM0t.r for the redpoll data
• lnlOtis.r: Code to draw 2D contour plots of the lnL surface and to compute and overlay a quadratic approximation
• Intro.Rmd: R Markdown file to produce the Intro to RMark document. All my Rcode is in that document.
• skink.r: My code to answer the Intro to RMark questions

Part 4: hierarchical models

• Introduction to using JAGS and rjags, with exercises
• R code to fit linear regression model to grizzly bear data
• BUGS code for linear regression model
• R code to fit state space linear regression model to grizzly bear data
• BUGS code for state space linear regression model
• R code to fit exponential growth, process + obs error model to grizzly bear data
• BUGS code for exponential growth, process + obs error model
• BUGS code for exponential growth, process + obs error model with an additional covariate
• RStan code for linear regression
• RStan code for exponential growth, process + obs error model