Please note: active development has moved to packages 'validate'
    and 'errorlocate'. Facilitates reading and manipulating (multivariate) data
    restrictions (edit rules) on numerical and categorical data. Rules can be
    defined with common R syntax and parsed to an internal (matrix-like format).
    Rules can be manipulated with variable elimination and value substitution
    methods, allowing for feasibility checks and more. Data can be tested against
    the rules and erroneous fields can be found based on Fellegi and Holt's
    generalized principle. Rules dependencies can be visualized with using the
    'igraph' package.

A client for the Environmental Data Initiative repository REST API. The 'EDI' data repository <https://portal.edirepository.org/nis/home.jsp> is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the 'PASTA+' software stack <https://pastaplus-core.readthedocs.io/en/latest/index.html#> and was developed in collaboration with the US 'LTER' Network <https://lternet.edu/>. 'EDIutils' includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users.

Error-driven learning (based on the Widrow & Hoff (1960)<https://isl.stanford.edu/~widrow/papers/c1960adaptiveswitching.pdf> learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, <doi:10.1037/a0023851>) can be used to explain bottom-up human learning (Hoppe et al, <doi:10.31234/osf.io/py5kd>), but is also at the core of artificial neural networks applications in the form of the Delta rule. This package provides a set of functions for building small-scale simulations to investigate the dynamics of error-driven learning and it's interaction with the structure of the input. For modeling error-driven learning using the Rescorla-Wagner equations the package 'ndl' (Baayen et al, 2011, <doi:10.1037/a0023851>) is available on CRAN at <https://cran.r-project.org/package=ndl>. However, the package currently only allows tracing of a cue-outcome combination, rather than returning the learned networks. To fill this gap, we implemented a new package with a few functions that facilitate inspection of the networks for small error driven learning simulations. Note that our functions are not optimized for training large data sets (no parallel processing), as they are intended for small scale simulations and course examples. (Consider the python implementation 'pyndl' <https://pyndl.readthedocs.io/en/latest/> for that purpose.)