Package index • tulpaObs

Front door

One fitter; the model is chosen by the family object.

tobs(): Fit a hierarchical latent-state observation model
tobs_data(): Convert long-format data to a site x visit observation object
tobs_format(): Format occupancy data from matrices/lists
tobs_format_ms(): Format multi-species occupancy data
tobs_check_id(): Check model identifiability
tobs_terms icar bym2 car_proper gp multiscale_gp spde svc re temporal latent spatial: Structured terms inside a tobs() formula
tobs_get(): Extract one species' fit from a batched occu_cover fit.
print(<tobs_family>): Print method for tobs_family

Occupancy families

occu(): Single-season occupancy family
dyn_occu(): Dynamic (multi-season) occupancy family
int_occu(): Integrated occupancy family
dyn_int_occu(): Multi-season integrated occupancy family
ms_occu(): Multispecies (community) single-season occupancy family
ms_dyn_occu(): Community (multispecies) dynamic occupancy family
ms_int_occu(): Community (multispecies) integrated occupancy family
jsdm(): Joint species distribution family (no detection process)
t_occu(): Multi-season occupancy with a temporal (AR1) year effect (tPGOcc)
fp_occu(): Multistate false-positive occupancy family
fp_occu_laplace(): Maximum-likelihood fit of the multistate false-positive occupancy model
royle_nichols(): Royle-Nichols occupancy family
occu_ttd(): Time-to-detection occupancy family
occu_multi(): Multi-species co-occurrence occupancy family
occu_categorical(): Presence + nominal class hurdle family
occu_priors(): Weakly-informative priors for occupancy Laplace fits

Abundance, distance and removal families

abun(): N-mixture abundance family
ms_abun(): Multispecies N-mixture family
dyn_abun(): Open-population (Dail-Madsen) N-mixture family
dyn_abun_laplace(): Maximum-likelihood fit of the Dail-Madsen open N-mixture
count(): Count / relative-abundance GLMM family (no detection process)
ms_count(): Multispecies (community) relative-abundance GLMM family
distance(): Binned distance-sampling family
distance_laplace(): Laplace fit of the binned distance-sampling abundance model
ms_distance(): Community (multispecies) binned distance-sampling family
distsamp_open(): Open-population distance-sampling family
gdistremoval(): Joint distance + removal sampling family
removal(): Removal-sampling family
removal_laplace(): Laplace fit of the removal-sampling abundance model
double_observer(): Double-observer abundance family
nmix_laplace(): Laplace fit of the Royle (2004) N-mixture model
nmix_laplace_bym2(): BYM2 Royle (2004) N-mixture model via nested Laplace
nmix_laplace_car_proper(): Proper CAR Royle (2004) N-mixture model via nested Laplace
nmix_laplace_icar(): Spatial Royle (2004) N-mixture model via nested Laplace
nmix_laplace_re(): Community / multispecies N-mixture by Laplace
nmix_site_marginal(): Per-site N-mixture marginal as a composable random-effect callback

Cover families

cover(): Cover hurdle family (vegetation cover, MOTIVATE pattern)
occu_cover(): Joint occupancy-detection + cover hurdle family
occu_cover_inputs(): Build occu_cover() model inputs from a long, plot-level frame
ms_occu_cover(): Community (multispecies) joint occupancy-detection + cover family
occu_multiscale_cover(): Three-level (multiscale) occupancy + cover hurdle family
cover_priors(): Weakly-informative priors for cover-hurdle Laplace fits
occu_aggregation_scan(): Suggest spatial and temporal aggregation for an identifiable occupancy model
plot(<tobs_aggregation_scan>): Plot an occupancy aggregation scan

Fit accessors

S3 methods on tobs_fit.

print(<tobs_fit>): Print method for tobs_fit
summary(<tobs_fit>): Summary for tobs_fit, with skewness column when simplified Laplace is used
coef(<tobs_fit>): Coefficients for a tobs_fit
coef(<tobs_batch>): Per-species coefficients from a batched occu_cover fit.
print(<tobs_batch>): Print a batched multi-response occu_cover fit.
tidy(<tobs_fit>): Coefficient table for a tobs_fit
glance(<tobs_fit>): One-row model summary for a tobs_fit
fitted(<tobs_fit>): Fitted values (occupancy and detection probabilities)
residuals(<tobs_fit>): Residuals from occupancy model
ranef(<tobs_fit>): Random-effect estimates (BLUPs) for a tobs_fit
logLik(<tobs_fit>): Log-likelihood of a tobs fit
nobs(<tobs_fit>): Number of observations
update(<tobs_fit>): Update and refit a tobs model
predict(<tobs_fit>): Predict from occupancy model
predict(<cover_fit>): Predict cover from a cover_fit
predict(<occu_categorical_fit>): Predict from an occu_categorical fit
simulate(<tobs_fit>): Simulate replicate datasets from the posterior
`$`(<tobs_fit>): Access spOccupancy-compatible fields from tobs fits
tobs_predict_spatial(): Predict the state process at new spatial locations
tobs_marginal_effect(): Compute marginal effect of a covariate
tobs_richness(): Estimate species richness from community model
tobs_associations(): Residual species-association matrices from a spatial-factor community fit
nobs(<tobs_multiarm_fit>) coef(<tobs_multiarm_fit>) vcov(<tobs_multiarm_fit>) confint(<tobs_multiarm_fit>) logLik(<tobs_multiarm_fit>) glance(<tobs_multiarm_fit>) tidy(<tobs_multiarm_fit>) summary(<tobs_multiarm_fit>) plot(<tobs_multiarm_fit>) fitted(<tobs_multiarm_fit>) residuals(<tobs_multiarm_fit>): S3 methods for multi-arm fits

Diagnostics and model comparison

check_model(<tobs_fit>): Comprehensive model checking
pit_residuals(<tobs_fit>): PIT residuals for a tobs fit
ppc(): Posterior predictive check
sbc(<tobs_fit>): Simulation-based calibration for a fitted tobs model
test_dispersion(<tobs_fit>) test_zero_inflation(<tobs_fit>) test_outliers(<tobs_fit>): Goodness-of-fit tests for a tobs fit
waic(<tobs_fit>) loo(<tobs_fit>) dic(<tobs_fit>) cpo(<tobs_fit>) pointwise_loglik(<tobs_fit>): Model criteria for occupancy / cover models
tobs_stack() print(<tobs_stack>) fitted(<tobs_stack>) predict(<tobs_stack>): Stack fitted models into a LOO-weighted ensemble
convergence() converged(): Convergence record for a fitted model

Simulation

simulate_abun(): Simulate Royle (2004) N-mixture abundance data
simulate_count(): Simulate count / relative-abundance GLMM data
simulate_cover(): Simulate cover-hurdle data (lognormal positive part)
simulate_cover_joint(): Simulate cover-hurdle data with a shared BYM2 spatial field
simulate_distance(): Simulate binned distance-sampling abundance data
simulate_distsamp_open(): Simulate an open-population distance-sampling data set
simulate_double_observer(): Simulate a double-observer abundance data set
simulate_dyn_abun(): Simulate Dail-Madsen open N-mixture data
simulate_dyn_int_occu(): Simulate a multi-season integrated occupancy data set
simulate_dyn_occu(): Simulate temporal (multi-season) occupancy data
simulate_fp_occu(): Simulate multistate false-positive occupancy data
simulate_gdistremoval(): Simulate a joint distance + removal data set
simulate_int_occu(): Simulate integrated (multi-source) occupancy data
simulate_jsdm(): Simulate joint species distribution (presence/absence) data
simulate_ms_abun(): Simulate community (multispecies) N-mixture abundance data
simulate_ms_count(): Simulate community relative-abundance (count / continuous) data
simulate_ms_distance(): Simulate community (multispecies) binned distance-sampling data
simulate_ms_dyn_occu(): Simulate temporal multi-species occupancy data
simulate_ms_int_occu(): Simulate integrated multi-species occupancy data
simulate_ms_occu(): Simulate multi-species occupancy data
simulate_ms_occu_cover(): Simulate community (multispecies) joint occupancy-cover data
simulate_ms_occu_cover_spatial(): Simulate a reduced-rank spatial-factor community occu_cover data set (K = 1)
simulate_occu(): Simulate single-species occupancy data
simulate_occu_categorical(): Simulate from an occu_categorical model
simulate_occu_cover(): Simulate joint occupancy-detection + cover data
simulate_occu_multi(): Simulate a multi-species co-occurrence occupancy data set
simulate_occu_multiscale_cover(): Simulate a three-level occupancy + cover hurdle data set
simulate_occu_ttd(): Simulate a time-to-detection occupancy data set
simulate_removal(): Simulate removal-sampling abundance data
simulate_royle_nichols(): Simulate Royle-Nichols occupancy data
simulate_t_occu(): Simulate multi-season occupancy with an AR1 year effect (tPGOcc)

Data and utilities

foray_counts: Repeated point-count abundance survey (synthetic)
meadow_cover: Grassland vegetation-cover panel (synthetic)
peatland_occu: Peatland occupancy survey (synthetic)
summary(<tobs_data>): Summarise occupancy data
plot(<tobs_data>): Plot detection history patterns
within_between(): Within / between (Mundlak) decomposition for longitudinal data