1. Binomial SCR Model with half-normal plateau detection function | 1.1 Habitat and trapping grid | 1.2 Define SCR model with half-normal plateau detection function | 1.3 Set parameter values to simulate | 1.4 Create data, constants and inits objects | 1.5 Create NIMBLE model | 1.6 Simulate SCR data from the NIMBLE model | 1.7. MCMC with NIMBLE | 1.7.1 Compile the nimble model and create MCMC configuration | 1.7.2 Update the 'adaptive' arguments of MCMC samplers for sigma and w | 1.7.3 Run MCMC | 1.8. Computation of home range size | 2. Calculating home range radius and area with other detection functions | Half-normal (detFun = 0) | Exponential (detFun = 2) | Asymmetric logistic (detFun = 3) | Bimodal (detFun = 4) | Donut (detFun = 5) | REMARK | REFERENCES

1. Simulate SCR data | 1.1 Habitat and trapping grid | 1.2 Rescale coordinates | 1.3 Model definition | 1.4 Set up parameter values | 1.5 Create data, constants and initial values | 1.6 Create NIMBLE model | 1.7 Simulate data | 2. Fit model with data augmentation | 2.1. Prepare the input data | 2.2. Run MCMC with NIMBLE | 3. Fit model without data augmentation | 3.1. Model definition | 3.2. Prepare the input data | 3.3. Run MCMC with NIMBLE | REFERENCES

Load Libraries | Define 'nimbleSCR' Models | Load Wolverine Data | Create NIMBLE Model | Configure and Build MCMC | Compile and Run MCMC | Results | Conclusion | References

1. SIMULATE BINOMIAL SCR DATA | 1.1 Habitat and trapping grid | 1.2 Rescale coordinates and local evaluation | 1.3 Define model code | 1.4 Define parameter values to simulate | 1.5 Create data, constants and inits objects | 1.6 Create NIMBLE model | 1.7 Simulate SCR data from the NIMBLE model | 2. RUN MCMC WITH NIMBLE | 3. SIMULATE BINOMIAL SCR DATA WITH TRAP COVARIATES ON $p_0$ | 3.1 Simulate trap covariates | 3.2 Define model code | 3.3 Create NIMBLE model | 3.4 Simulate SCR from the nimble model | 4. RUN MCMC WITH NIMBLE | REFERENCES

Load Libraries | Load nimbleSCR package | Define Model Structure | Load and Process Data | Use of custom distribution to increase MCMC efficiency | 1. Local detector evaluation | 2. Sparse representation of the observation matrix | 3. Skip unnecessary calculations | Create NIMBLE Model | Configure and Build MCMC | Block sampling to increase MCMC efficiency | Compile and Run MCMC | Results | References