Package index • drmr

All functions

ages_edens() lambda_drm(): Age-specific expected densities based on DRM.
apply_movement(): Simple movement to population dynamics
between(): Check if elements in x are between corresponding elements in lb and ub
between_scalar(): Check if elements in x are between corresponding elements in lb and ub
check_between(): Check if x is between lb and ub
clean_edens(): Cleaning the variable output from stan for interpretability.
default_algo(): Default arguments for inference algorithm
default_laplace(): Default Laplace arguments
default_nuts(): Default NUTS arguments
default_opt(): Default Optimization arguments
default_pf(): Default Pathfinder arguments
default_priors(): Default priors' hyperparameters
default_toggles(): Default toggles
default_vb(): Default VB arguments
draws(): Draws method for adrm and sdm objects.
drmr-package drmr: drmr: Dynamic Range Models in Stan
dtn(): Density of a truncated Normal distribution
dtt(): Density of a truncated Student's t distribution
elpd(): (out-of-sample) Expected Log-posterior Density (ELPD) based on adrm and sdm objects.
fit_drm(): Fit the dynamic range model.
fit_sdm(): Fit a GLM based SDM
fitted(<adrm>): DRM fitted values
fitted(<sdm>): SDM fitted values
fitted_pars_drm(): Retrieve parameters needed for predictions
fitted_pars_lambda(): Retrieve parameters needed for predictions
fitted_pars_ll(): Retrieve parameters needed for predictions
fitted_pars_sdm(): Retrieve parameters needed for predictions
fix_linbeta(): Regression coefficient for non-centered variable
fix_re(): Random effects verbose to code
gen_adj(): Generates an adjacency matrix
get_fitted_pars(): Retrieve parameters needed for predictions
get_nodes(): Get nodes for ICAR spatial random effects
get_phi_hat(): Estimate phi
get_scaling(): Scaling factor for ICAR
get_zeros(): Zeros and nonzeros
ginv(): Generalized Inverse
int_score(): Calculate the interval score
lambda2df(): Turn an array of density per age, time, and patch/site into a data.frame
log_lik(): Computing the log-likelihood function fot adrm and sdm objets.
make_data(): Make data for DRM stan models
make_data_sdm(): Make data for SDM stan models
make_surv(): Generate the "survival" terms
marg(): Marginal Relationships with Covariates
max_quad_x(): Value of a covariate that maximizes the response variable in a quadratic model.
model_sim(): Generate a random sample from a model's predictive distribution given a set of parameters
new_adrm(): Create an adrm object
new_aesd(): Create a aesd object
new_pred_drmr(): Create a pred_drmr object
new_sdm(): Create an sdm object
pars_transform(): Transform parameters to a meaningful and interpretable scale.
plot(<marg_adrm>): Plot Marginal Relationships for ADRM Objects
pop_dyn(): Simulate population dynamics
pp_sim(): Generate samples from the prior predictive distribution of model parameters
predict(<adrm>) predict_drm(): Predictions based on DRM.
predict(<sdm>) predict_sdm(): Predictions based on SDM.
print(<drmrmodels>): Print method for adrm and sdm objects
print(<summary.drmrmodels>): Print method for summary.spatial_model
prior_inits(): Generate initial values for MCMC from the prior
prior_sample(): Generate samples from the prior distribution of model parameters
rtn(): Random number generation from a truncated Normal distribution
rtt(): Random number generation from a truncated Student's t distribution
safe_modify(): Modifying a named list
sim_ar(): Simulate AR(1)
sim_dens(): Simulate response variable
sim_log_rec(): Simulate log-recruitment
sum_fl: Summer Flounder
summarise_adens(): Extract and Summarize Age-Specific Densities
summary(<CmdStanGQ>): Wrapper for the summary method for CmdStanGQ objects
summary(<aesd>): Summary method for aesd objects
summary(<drmrmodels>): Summary method for adrm and sdm objects
summary(<pred_drmr>): Summary method for pred_drmr objects
update(<adrm>): Update and Re-fit a DRM Model
update(<sdm>): Update and Re-fit a SDM Model