Installs an updated version of 'pomdp-solve' and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.

Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) <doi:10.1016/j.ecolmodel.2009.05.003>.

Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models).  The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a versatile platform for implementation of inference methods for general POMP models.

An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). 
    Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. 
    Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. 
    Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. 
    Lutkepohl, H. (2007, ISBN:3540262393).