Package index • drmr

All functions

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

default_priors(): Default priors' hyperparameters

default_toggles(): Default toggles

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

fit_drm(): Fit the dynamic range model.

fit_sdm(): Fit a GLM based SDM

fitted_pars_drm(): Retrieve parameters needed for forecasting

fitted_pars_lambda(): Retrieve parameters needed for forecasting

fitted_pars_sdm(): Retrieve parameters needed for forecasting

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 forecasting

get_nodes(): Get nodes for ICAR spatial random effects

get_phi_hat(): Estimate phi

get_scaling(): Scaling factor for ICAR

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

lambda_drm(): Age-specific densities based on DRM.

make_data(): Make data for DRM stan models

make_data_sdm(): Make data for SDM stan models

make_surv(): Generate the "survival" terms

marg_rec() marg_pabs() marg_surv(): 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

pars_transform(): Transform parameters to a meaningful and interpretable scale.

pop_dyn(): Simulate population dynamics

pp_sim(): Generate samples from the prior predictive distribution of model parameters

predict_drm(): Forecasts based on DRM.

predict_sdm(): Forecasts based on SDM.

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