Create a model specification

Define a parametric model through its log-likelihood function. This is the foundational object for equivalence and identifiability analysis.

Usage

model_spec(
  loglik_fn,
  par_names,
  par_bounds = NULL,
  name = NULL,
  validate = TRUE
)

Arguments

loglik_fn

A function with signature function(y, ...) where y is the data and ... are named parameters. Must return a scalar log-likelihood.

par_names

Character vector of parameter names. Must match the argument names in loglik_fn (excluding y).

par_bounds

Optional named list of parameter bounds. Each element should be a length-2 numeric vector c(lower, upper). Parameters not specified default to c(-Inf, Inf).

name

Optional character string naming the model. If NULL, a name is generated from the parameter names.

validate

Logical; if TRUE (default), validate inputs on construction. Set to FALSE for performance in tight loops.

Value

An S3 object of class model_spec with components:

loglik_fn

The log-likelihood function

par_names

Character vector of parameter names

par_bounds

Named list of bounds for each parameter

n_par

Number of parameters

name

Model name

Examples

# Exponential model
exp_spec <- model_spec(
  loglik_fn = function(y, lambda) sum(dexp(y, rate = lambda, log = TRUE)),
  par_names = "lambda",
  par_bounds = list(lambda = c(1e-6, Inf)),
  name = "Exponential"
)

# Normal model with two parameters
norm_spec <- model_spec(
  loglik_fn = function(y, mu, sigma) {
    sum(dnorm(y, mean = mu, sd = sigma, log = TRUE))
  },
  par_names = c("mu", "sigma"),
  par_bounds = list(sigma = c(1e-6, Inf))
)