Description: Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Processes (POMDP) models. The package includes pomdp-solve to solve POMDPs using a variety of exact and approximate value iteration algorithms. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.

Suggests: knitr, rmarkdown, sarsop, testthat
Author: Michael Hahsler [aut, cph, cre],

Suggests: knitr, rmarkdown, testthat, Ternary, visNetwork, sarsop

Description: Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.
Copyright: Copyright (C) Hossein Kamalzadeh and Michael Hahsler.
'POMDP.R' 'MDP.R' 'Maze.R' 'Tiger.R' 'colors.R'

Title: Infrastructure for Partially Observable Markov Decision

Copyright: Copyright (C) Michael Hahsler and Hossein Kamalzadeh.

Title: Interface to 'pomdp-solve' for Partially Observable Markov
Description: Installs 'pomdp-solve', a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.

Description: Installs an updated version of 'pomdp-solve', 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 package pomdp. Kaelbling, Littman and Cassandra (1998) <doi:10.1016/S0004-3702(98)00023-X>.

Title: Predictive Power of Linear and Tree Modeling
Description: 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.
Imports: tibble, dplyr, readr, caret, gbm, stats, randomForest, pROC,