Struct GeneralisedLogistic 

pub struct GeneralisedLogistic;

Generalised logistic (Richards) curve.

y = amplitude / (1 + exp(shift − rate·x))^(1/shape)

Parameters (in order): [amplitude, shift, rate, shape]

One kernel covers two NIST StRD datasets, because the plain logistic is this form at shape = 1:

• Rat43 is the full four-parameter Richards curve.
• Rat42 is b1/(1 + exp(b2 − b3·x)): hold shape fixed at 1 and the other three map straight onto NIST’s b1..b3.

Domain guard: shape must be non-zero, and 1 + exp(...) is always > 1 so the power is well defined for finite arguments. A zero shape returns f64::NAN rather than an infinity, so the solver backs off instead of poisoning the residual vector.

Analytic Jacobian (let u = exp(shift − rate·x), s = 1 + u, q = s^(−1/shape)):

• ∂y/∂amplitude = q
• ∂y/∂shift = −amplitude·q·u / (shape·s)
• ∂y/∂rate = amplitude·q·u·x / (shape·s)
• ∂y/∂shape = amplitude·q·ln(s) / shape²

Trait Implementations

impl Model for GeneralisedLogistic

fn eval(&self, x: &[f64], p: &[f64]) -> f64

Evaluate the model at coordinate x with the given params.

fn jacobian_into(&self, x: &[f64], p: &[f64], out: &mut [f64])

Fill a pre-allocated slice with Jacobian values (one entry per parameter).

fn jacobian(&self, x: &[f64], p: &[f64]) -> Vec<f64>

Jacobian — one derivative per parameter, in the same order as param_names().

fn param_names(&self) -> Vec<Cow<'static, str>>

Ordered parameter names, matching the layout expected by eval and jacobian.

fn eval_slice_into(&self, xs: &[f64], params: &[f64], out: &mut [f64])

Batch evaluation of a 1-D model: fill out[i] = eval([xs[i]], params).

fn jac_slice_into(&self, xs: &[f64], params: &[f64], out: &mut [f64])

Batch Jacobian for a 1-D model: fill out in row-major layout [i * params.len() + j] = d(model)/d(params[j]) at xs[i].

fn n_dims(&self) -> usize

Number of coordinate dimensions consumed from x. Defaults to 1.