This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication.

Create, upload and run 'Acumos' R models.
  'Acumos' (<https://www.acumos.org>) is a platform and open source framework intended to make it easy to build,
  share, and deploy AI apps. 'Acumos' is part of the 'LF AI Foundation', an umbrella
  organization within 'The Linux Foundation'. With this package, user can create a
  component, and push it to an 'Acumos' platform.

Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>.

Functions for calculating the acute chronic workload ratio using three 
            different methods: exponentially weighted moving average (EWMA), rolling 
            average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this 
            methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589>
            for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.

Performs discrete, real, and gentle boost under both exponential and 
             logistic loss on a given data set.  The package ada provides a straightforward, 
             well-documented, and broad boosting routine for classification, ideally suited 
             for small to moderate-sized data sets.

It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging
	algorithm using classification trees as individual classifiers. Once these classifiers have been
	trained, they can be used to predict on new data. Also, cross validation estimation of the error can
	be done. Since version 2.0 the function margins() is available to calculate the margins for these
	classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument
	of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu 
	et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as
	a function of the number of iterations. In addition, the ensembles can be pruned using the option 
	'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of
	each class for observations can be obtained. Version 3.1 modifies the relative importance measure
	to take into account the gain of the Gini index given by a variable in each tree and the weights of 
	these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo
	and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also 
	available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on 
	unlabeled data. Version 4.2 includes the parallel computation option for some of the functions. 
	Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandra
	and Plaia, 2023).