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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 scripts
agrdatasci
gosset:Tools for Data Analysis in Experimental Agriculture
Methods to analyse experimental agriculture data, from data synthesis to model selection and visualisation. The package is named after W.S. Gosset aka ‘Student’, a pioneer of modern statistics in small sample experimental design and analysis.
Maintained by Kauê de Sousa. Last updated 4 months ago.
experimental-designrankings-data
6 stars 6.44 score 23 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 scripts
cran
SCCI:Stochastic Complexity-Based Conditional Independence Test for Discrete Data
An efficient implementation of SCCI using 'Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.
Maintained by Alexander Marx. Last updated 6 years ago.
1.00 score