Description: Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.

Description: Computes the euclidean distance between rows of a matrix X
and rows of another matrix Y.  Previously, this could only be
done by binding the two matrices together and calling 'dist',
but this creates unnecessary computation by computing the
distances between a row of X and another row of X, and likewise

and rows of another matrix Y.  Previously, this could be done
by binding the two matrices together and calling 'dist', but
this creates unnecessary computation by computing the distances
between a row of X and another row of X, and likewise for Y.
pdist strictly computes distances across the two matrices, not
within the same matrix, making computations significantly

Title: Proximal/Distal Modeling Framework for Pavlovian Conditioning
Description: Fits a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.

Title: Proximal/distal modeling framework for Pavlovian conditioning
Description: This package provides a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.