This function derives the gradients of the negative log likelihood function, with respect to all parameters. It is based on the theory presented in Introduction to Distance Sampling (2001) and Distance Sampling: Methods and Applications (2015). It is not meant to be called by users of the mrdsand Distance packages directly but rather by the gradient-based solver. This solver is use when our distance sampling model is for single-observer data coming from either line or point transect and only when the detection function contains an adjustment series but no covariates. It is implement for the following key + adjustment series combinations for the detections function: the key function can be half-normal, hazard-rate or uniform, and the adjustment series can be cosine, simple polynomial or Hermite polynomial. Data can be either binned or exact, but a combination of the two has not been implemented yet.
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