Introduction | What is ubms? | Why should you use it? | What are the disadvantages? | Example: Fitting a single-season occupancy model | Set up the data | Fit a model | Fit the model in unmarked | Fit the model in ubms | Compare results | Understanding the ubms output summary | Extracting individual parameters | Compare candidate models | Fit the models | Compare the models | Diagnostics and model fit | MCMC diagnostics | Model fit | Model inference | Marginal covariate effects | Predict parameter values | Simulate latent occupancy states | References

Prerequisites and advices | Organise the input data: design the unmarkedFrame object | Define the unmarkedFrame subclass for this model | Write the function that creates the unmarkedFrame object | Write the S4 methods associated with the unmarkedFrame object | Specific methods | Generic methods | Methods to access new attributes | Fitting the model | Inputs of the fitting function | Read the unmarkedFrame object: write the getDesign method | The likelihood function | The R likelihood function: easily understandable | The C++ likelihood function: faster | The TMB likelihood function: for random effects | Organise the output data | unmarkedEstimate objects per submodel | Design the unmarkedFit object | Test the complete fitting function process | Write the methods associated with the unmarkedFit object | getP | simulate | plot | Update the NAMESPACE file | Write tests | Write documentation | Add to unmarked

Abstract | Overview of unmarked | Typical unmarked session | Importing and formatting data | Fitting models | Back-transforming parameter estimates | Model selection and model fit | Derived parameters and empirical Bayes methods | References

Abstract | Introduction | Dynamic occupancy models | Ecological or state process | Observation process | Modeling of parameters | Dynamic occupancy models for simulated data | Simulating, formatting, and summarizing data | Model fitting | Manipulating results: prediction and plotting | Derived parameters | Goodness-of-fit | Dynamic occupancy models for crossbill data from the Swiss MHB | The crossbill data set | Importing, formatting, and summarizing data | Model selection | Manipulating results: Prediction and plotting | References

Introduction | LM: A macro for complete linear models | Example logistic regression with mtcars | Example linear mixed model using ChickWeights | LINPRED | Generate a linear predictor from a formula | Factor/categorical covariates | Add a link function | Functions in formulas | Get default priors | Suppress the macro comments | Random effects | Include LINPRED in a complete model | LINPRED_PRIORS | FORLOOP | Writing Your Own Macros | Manually creating the macro | Using buildMacro to create the macro

Introduction | Example dataset | Organize the dataset | Detection-nondetection data | Species covariates | Site covariates | Observation covariates | Create unmarkedFrame | Fit a model | Estimates of random intercepts and slopes | Get estimates of occupancy | Plot covariate relationships by species | Richness | Handling guilds or similar groups | Dividing the data by guild | Fit separate models by guild | Calculate richness

Hypothesis Testing | Wald tests | Making a conclusion | Types of error | Introduction | Inputs | The unmarkedFrame | The model type | Other arguments | Call powerAnalysis | Effect sizes | Run the power analysis | Run the power analysis for a smaller sample size | Compare power across several sample sizes | Conclusion | References

Abstract | Introduction | Data | Closed Population Models | Models $M_0$, $M_t$, and models with covariates of $p$. | Modeling behavioral responses, Model $M_b$ | Caution, Warning, Danger | Individual Heterogeneity in Capture Probability, Model $M_h$ | Distance-related heterogeneity | Modeling Temporary Emigration | References

Abstract | Introduction | Importing, formatting, and summarizing data | Model fitting | Manipulating results | Prediction and plotting | Model extensions | Conclusion | References

Introduction | Example | Format the input data | Choose RSR options | Fit the model | Examine results | Plotting | Model selection | Compare with stocc | References

Introduction | Components of a call to simulate | Simulating an occupancy dataset | 1. The unmarkedFrame | 2. Specify the model type | 3. Specify other arguments to the fitting function | 4. Specify the corresponding parameter values | Run simulate | Fit a model to the simulated dataset | Simulating a more complex dataset: gdistremoval | 2. Arguments sent to gdistremoval | 3. Coefficient values | 4. Run simulation | Fit model to simulated dataset | Conclusion | References

Installing JAGS | Typical jagsUI Workflow | 1. Organize data | 2. Write BUGS model file | 3. Initial values | 4. Parameters to monitor | 5. MCMC settings | 6. Run JAGS | 7. Examine output | Diagnostics | Posteriors | Update

Introduction | Simple multispecies analysis | Formatting the data | Occupancy formulas | Detection formulas | Fit the model | Occupancy probabilities | Marginal occupancy | Plotting marginal occupancy | Conditional occupancy | Plotting conditional occupancy | Multispecies model with covariates | Add housing density as a covariate | Plotting covariate effects | Model selection | Model fitting challenges | Penalized likelihood | Penalized likelihood with occuMulti | References

Introduction | Simulating the data | Fitting the model in ubms | Fitting the model in JAGS | Compare fixed effect estimates | Compare random effect estimates

Abstract | Mapping Occurrence Probability | Mapping Population Density | References

Introduction | "Stacked" Models | Fitting a stacked model with ubms | Read in the input data | Convert the input data to stacked format | Fit the Stacked Model | Accessing the random intercepts | References