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| calc_linex_a | estimate optimal 'a' parameter for linex loss function |
| convert.HDS.to.mcmc | function to convert HierarchicalDS MCMC list vector (used in estimation) into an mcmc object (cf. coda package) |
| generate_inits | generate initial values for MCMC chain if not already specified by user |
| generate_inits_misID | generate initial values for misID model if not already specified by user |
| get_confusion_array | Fill confusion array - one confusion matrix for each individual (DEPRECATED) |
| get_confusion_mat | Fill a list with confusion matrices for each record |
| get_mod_matrix | function to produce a design matrix given a dataset and user-specified formula object |
| hierarchical_DS | Primary function for hierarchical, areal analysis of distance sampling data (without movement). This function pre-processes data and calls other functions to perform the analysis, and is the only function the user needs to call themselves. |
| linear_adj | Produce an adjacency matrix for a vector |
| log_lambda_gradient | compute the first derivative of log_lambda likelihood component for Langevin-Hastings |
| log_lambda_log_likelihood | compute the likelihood for nu parameters |
| mcmc_ds | Function for MCMC analysis |
| plot_N_map | function to plot a map of abundance. this was developed for spatio-temporal models in mind |
| plot_obs_pred | plot 'observed' versus predicted values for abundance of each species at each transect |
| post_loss | function to calculate posterior predictive loss given the output object from hierarchicalDS |
| probit.fct | Mrds probit detection and related functions |
| rect_adj | Produce an RW1 adjacency matrix for a rectangular grid for use with areal spatial models (queens move) |
| rect_adj_RW2 | Produce an RW2 Adjacency matrix for a rectangular grid for use with areal spatial models. This formulation uses cofficients inspired by a thin plate spline, as described in Rue & Held, section 3.4.2 Here I'm outputting an adjacency matrix of 'neighbor weights' which makes Q construction for regular latices easy to do when not trying to make inference about all cells (i.e., one can just eliminate rows and columns associated with cells one isn't interested in and set Q=-Adj+Diag(sum(Adj)) |
| rrw | SIMULATE AN ICAR PROCESS |
| simdata | MCMC output from running example in Hierarchical DS |
| simulate_data | function to simulate double observer spatial distance sampling data subject to possible zero inflation and species misidentification |
| sim_out | MCMC output from running example in Hierarchical DS |
| square_adj | Produce an adjacency matrix for a square grid |
| stack_data | function to stack data (going from three dimensional array to a two dimensional array including only "existing" animals |
| stack_data_misID | function to stack data for midID updates (going from four dimensional array to a two dimensional array including observed groups |
| summary_N | calculate parameter estimates and confidence intervals for various loss functions |
| switch_pdf | function to calculate the joint pdf for a sample of values from one of a number of pdfs |
| switch_sample | function to sample from a specified probability density function |
| switch_sample_prior | function to sample from hyperpriors of a specified probability density function; note that initial values for sigma of lognormal random effects are fixed to a small value (0.05) to prevent numerical errors |
| table.mcmc | function to export posterior summaries from an mcmc object to a table |