biexp_core: Biexponential model with gradient

In mnirs: Muscle Near-Infrared Spectroscopy Processing and Analysis

Biexponential model with gradient

Description

biexp_core() evaluates the curve and its partial derivatives on the canonical parameters. biexp_model() is the model function of SSbiexponential(): biexponential() plus the gradient for the parameters written as bare symbols in the call (see free_params()), so stats::nls() skips stats::numericDeriv().

Usage

biexp_core(t, A, B, tau, B2, tau2, TD = NULL)

biexp_model(t, A, B, tau, B2, tau2, TD = NULL)

Arguments

t	A numeric vector of the predictor variable (time).
A	A numeric parameter for the starting value of the response variable (the t = 0 intercept).
B	A numeric parameter for the asymptote of the fast component; the value the fast response alone would approach.
tau	A numeric parameter for the fast time constant (\tau_1), in units of the predictor variable t. Dominates the initial steep response.
B2	A numeric parameter for the asymptote of the slow component; the stable plateau the response recovers toward as t approaches infinity.
tau2	A numeric parameter for the slow time constant (\tau_2), in units of the predictor variable t. Typically ⁠tau2 >> tau⁠.
TD	A numeric parameter for the time delay before the onset of the response, in units of the predictor variable t. If NULL (default), a 5-parameter model without time delay is used.

Value

biexp_core(): a list of the curve val and the partial derivatives by parameter name. biexp_model(): a numeric vector of predicted values with a "gradient" attribute when any parameter is free.

mnirs documentation built on Sept. 13, 2026, 1:06 a.m.