Data Preparation During and After the Use of the Experience Sampling Methodology (ESM) (esmprep)
Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in ‘esmprep’ are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.

Low-Level Interface to the ‘.NET’ Virtual Machine Along the Lines of the R C/Call API (rDotNet)
Low-level interface to ‘.NET’ virtual machine along the lines of the R C .call interface. Can create ‘.NET’ object, call methods, get or set properties, call static functions, etc.

Project Management Tools (projmanr)
Calculates the critical path for a series of tasks, creates Gantt charts and generates network diagrams in order to provide similar functionality to the basic tools offered by ‘MS Project’.

Dynamic Ensembles for Time Series Forecasting (tsensembler)
A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions ‘predict()’ and ‘forecast()’ to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as ‘update_weights()’ or ‘update_base_models()’. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. ‘Arbitrated Ensemble for Time Series Forecasting.’ to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: ‘Arbitrated Ensemble for Solar Radiation Forecasting.’ International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.

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