Showing 4 of total 4 results (show query)
mhahsler
pomdp:Infrastructure for Partially Observable Markov Decision Processes (POMDP)
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>.
Maintained by Michael Hahsler. Last updated 4 months ago.
control-theorymarkov-decision-processesoptimizationcpp
19 stars 7.03 score 21 scriptsmhahsler
markovDP:Infrastructure for Discrete-Time Markov Decision Processes (MDP)
Provides the infrastructure to work with Markov Decision Processes (MDPs) in R. The focus is on convenience in formulating MDPs, the support of sparse representations (using sparse matrices, lists and data.frames) and visualization of results. Some key components are implemented in C++ to speed up computation. Several popular solvers are implemented.
Maintained by Michael Hahsler. Last updated 15 days ago.
control-theorymarkov-decision-processoptimizationcpp
7 stars 5.51 score 4 scriptsmhahsler
rEMM:Extensible Markov Model for Modelling Temporal Relationships Between Clusters
Implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of Extensible Markov Model (EMM). TRACDS adds a temporal or order model to data stream clustering by superimposing a dynamically adapting Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN data stream clustering). Development of this package was supported in part by NSF IIS-0948893 and R21HG005912 from the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.
Maintained by Michael Hahsler. Last updated 7 months ago.
clusteringdata-streamsequence-analysis
2 stars 3.79 score 31 scripts
cran
ChannelAttribution:Markov Model for Online Multi-Channel Attribution
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
Maintained by Davide Altomare. Last updated 2 years ago.
24 stars 2.86 score 1 dependents