mhahsler r-universe repositoryhttps://mhahsler.r-universe.devPackage updated in mhahslercranlike-server 0.17.28https://github.com/mhahsler.png?size=400mhahsler r-universe repositoryhttps://mhahsler.r-universe.devSun, 16 Jun 2024 18:33:19 GMT[mhahsler] dbscan 1.1-12-1mhahsler@lyle.smu.edu (Michael Hahsler)A fast reimplementation of several density-based
algorithms of the DBSCAN family. Includes the clustering
algorithms DBSCAN (density-based spatial clustering of
applications with noise) and HDBSCAN (hierarchical DBSCAN), the
ordering algorithm OPTICS (ordering points to identify the
clustering structure), shared nearest neighbor clustering, and
the outlier detection algorithms LOF (local outlier factor) and
GLOSH (global-local outlier score from hierarchies). The
implementations use the kd-tree data structure (from library
ANN) for faster k-nearest neighbor search. An R interface to
fast kNN and fixed-radius NN search is also provided. Hahsler,
Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.https://github.com/r-universe/mhahsler/actions/runs/9538347004Sun, 16 Jun 2024 18:33:19 GMTdbscan1.1-12-1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/dbscandbscan.Rnwdbscan.pdfFast Density-based Clustering (DBSCAN and OPTICS)2017-02-02 23:02:442022-12-25 17:19:46hdbscan.Rmdhdbscan.htmlHDBSCAN with the dbscan package2017-03-17 21:04:252022-01-14 22:13:46[mhahsler] streamConnect 0.0-3mhahsler@lyle.smu.edu (Michael Hahsler)Adds functionality to connect stream mining components
from package stream using sockets and Web services. The package
can be used create distributed workflows and create
plumber-based Web services which can be deployed on most common
cloud services.https://github.com/r-universe/mhahsler/actions/runs/9469370448Tue, 11 Jun 2024 16:24:48 GMTstreamConnect0.0-3successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/streamConnectconnections.Rmdconnections.htmlstream: Working With Data Streams using Connections and Web Services2024-05-16 19:01:172024-06-11 16:24:05[mhahsler] markovDP 0.99.0mhahsler@lyle.smu.edu (Michael Hahsler)The package provides the infrastructure to work with 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. It
also implements several popular solvers.https://github.com/r-universe/mhahsler/actions/runs/9389328376Wed, 05 Jun 2024 18:05:29 GMTmarkovDP0.99.0successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/markovDPgridworlds.Rmdgridworlds.htmlGridworlds in Package markovDP2024-05-30 20:27:062024-06-05 18:05:29markovDP.RmdmarkovDP.htmlmarkovDP: Discrete-Time Markov Decision Processes (MDPs)2024-05-31 18:46:212024-06-05 18:05:29[mhahsler] pomdp 1.2.3-1mhahsler@lyle.smu.edu (Michael Hahsler)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>.https://github.com/r-universe/mhahsler/actions/runs/9215625856Thu, 23 May 2024 21:26:53 GMTpomdp1.2.3-1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/pomdpgridworlds.Rmdgridworlds.htmlGridworlds in Package pomdp2024-02-15 19:48:012024-02-22 18:32:28pomdp.Rmdpomdp.htmlpomdp: Introduction to Partially Observable Markov Decision Processes2024-02-15 19:48:012024-04-22 13:51:43[mhahsler] rMSA 0.99.1mhahsler@lyle.smu.edu (Michael Hahsler)Seamlessly interfaces the Multiple Sequence Alignment
software packages ClustalW, MAFFT, MUSCLE and Kalign
(downloaded separately) and provides support to calcualte
distances between sequences. This work was partially supported
by grant no. R21HG005912 from the National Human Genome
Research Institute.https://github.com/r-universe/mhahsler/actions/runs/9194084174Wed, 22 May 2024 14:53:27 GMTrMSA0.99.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/rMSArMSA.RnwrMSA.pdfInterface to Popular Multiple Sequence Alignment Tools2015-10-06 17:15:252015-10-06 17:15:25[mhahsler] stream 2.0-2.1mhahsler@lyle.smu.edu (Michael Hahsler)A framework for data stream modeling and associated data
mining tasks such as clustering and classification. The
development of this package was supported in part by NSF
IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et
al (2017) <doi:10.18637/jss.v076.i14>.https://github.com/r-universe/mhahsler/actions/runs/9533701169Fri, 17 May 2024 19:17:57 GMTstream2.0-2.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/streamextending_stream.Rnwextending_stream.pdfstream: Extending the stream Framework2022-05-24 14:46:202022-09-03 17:36:23stream.Rnwstream.pdfstream: Introduction to the package2015-12-06 02:45:552024-04-22 17:27:21[mhahsler] arulesCBA 1.2.7mhahsler@lyle.smu.edu (Michael Hahsler)Provides the infrastructure for association rule-based
classification including the algorithms CBA, CMAR, CPAR, C4.5,
FOIL, PART, PRM, RCAR, and RIPPER to build associative
classifiers. Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.https://github.com/r-universe/mhahsler/actions/runs/9511146516Wed, 15 May 2024 20:01:18 GMTarulesCBA1.2.7successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/arulesCBA[mhahsler] seriation 1.5.5.1mhahsler@lyle.smu.edu (Michael Hahsler)Infrastructure for ordering objects with an implementation
of several seriation/sequencing/ordination techniques to
reorder matrices, dissimilarity matrices, and dendrograms. Also
provides (optimally) reordered heatmaps, color images and
clustering visualizations like dissimilarity plots, and visual
assessment of cluster tendency plots (VAT and iVAT). Hahsler et
al (2008) <doi:10.18637/jss.v025.i03>.https://github.com/r-universe/mhahsler/actions/runs/9511146325Wed, 15 May 2024 16:17:14 GMTseriation1.5.5.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/seriationseriation.Rnwseriation.pdfAn Introduction to the R package seriation2015-10-11 04:20:572023-07-20 17:51:05[bioc] rBLAST 1.1.1mhahsler@lyle.smu.edu (Michael Hahsler)Seamlessly interfaces the Basic Local Alignment Search
Tool (BLAST) to search genetic sequence data bases. This work
was partially supported by grant no. R21HG005912 from the
National Human Genome Research Institute.https://github.com/r-universe/bioc/actions/runs/9510867611Tue, 30 Apr 2024 21:20:05 GMTrBLAST1.1.1successhttps://bioc.r-universe.devhttps://github.com/bioc/rBLASTblast.Rmdblast.htmlrBLAST: R Interface for the Basic Local Alignment Search Tool2024-03-22 17:26:192024-04-09 14:56:58[mhahsler] rBLAST 1.1.1mhahsler@lyle.smu.edu (Michael Hahsler)Seamlessly interfaces the Basic Local Alignment Search
Tool (BLAST) to search genetic sequence data bases. This work
was partially supported by grant no. R21HG005912 from the
National Human Genome Research Institute.https://github.com/r-universe/mhahsler/actions/runs/9297733271Tue, 30 Apr 2024 21:20:05 GMTrBLAST1.1.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/rBLASTblast.Rmdblast.htmlrBLAST: R Interface for the Basic Local Alignment Search Tool2024-03-22 17:26:192024-04-09 14:56:58[bioc] rRDP 1.39.0mhahsler@lyle.smu.edu (Michael Hahsler)This package installs and interfaces the naive Bayesian
classifier for 16S rRNA sequences developed by the Ribosomal
Database Project (RDP). With this package the classifier
trained with the standard training set can be used or a custom
classifier can be trained.https://github.com/r-universe/bioc/actions/runs/9525566454Tue, 30 Apr 2024 14:39:00 GMTrRDP1.39.0successhttps://bioc.r-universe.devhttps://github.com/bioc/rRDPrRDP.RmdrRDP.htmlrRDP: Interface to the RDP Classifier2024-03-26 17:32:502024-03-28 14:57:31[mhahsler] rRDP 1.37.3mhahsler@lyle.smu.edu (Michael Hahsler)This package installs and interfaces the naive Bayesian
classifier for 16S rRNA sequences developed by the Ribosomal
Database Project (RDP). With this package the classifier
trained with the standard training set can be used or a custom
classifier can be trained.https://github.com/r-universe/mhahsler/actions/runs/9280620417Sat, 27 Apr 2024 19:07:35 GMTrRDP1.37.3successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/rRDPrRDP.RmdrRDP.htmlrRDP: Interface to the RDP Classifier2024-03-26 17:32:502024-03-28 14:57:31[mhahsler] arulesViz 1.5.3mhahsler@lyle.smu.edu (Michael Hahsler)Extends package 'arules' with various visualization
techniques for association rules and itemsets. The package also
includes several interactive visualizations for rule
exploration. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.https://github.com/r-universe/mhahsler/actions/runs/9240753177Fri, 26 Apr 2024 13:12:06 GMTarulesViz1.5.3successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/arulesVizarulesViz.RnwarulesViz.pdfVisualizing Association Rules: Introduction to arulesViz2015-10-12 04:14:022024-04-25 18:52:37[mhahsler] rEMM 1.2.1mhahsler@lyle.smu.edu (Michael Hahsler)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>.https://github.com/r-universe/mhahsler/actions/runs/9170385963Sun, 21 Apr 2024 22:33:50 GMTrEMM1.2.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/rEMMrEMM.RnwrEMM.pdfExtensible Markov Model for data stream clustering2021-10-26 16:53:452022-05-31 16:58:17[mhahsler] streamMOA 1.3-1mhahsler@lyle.smu.edu (Michael Hahsler)Interface for data stream clustering algorithms
implemented in the MOA (Massive Online Analysis) framework
(Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard
Pfahringer (2010). MOA: Massive Online Analysis, Journal of
Machine Learning Research 11: 1601-1604).https://github.com/r-universe/mhahsler/actions/runs/9160219097Sat, 20 Apr 2024 18:10:56 GMTstreamMOA1.3-1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/streamMOAstreamMOA.RnwstreamMOA.pdfIntroduction to streamMOA2015-12-06 03:14:362024-04-20 18:10:56[mhahsler] qap 0.1-2.1mhahsler@lyle.smu.edu (Michael Hahsler)Implements heuristics for the Quadratic Assignment Problem
(QAP). Although, the QAP was introduced as a combinatorial
optimization problem for the facility location problem in
operations research, it also has many applications in data
analysis. The problem is NP-hard and the package implements a
simulated annealing heuristic.https://github.com/r-universe/mhahsler/actions/runs/9160219017Sat, 20 Apr 2024 17:52:51 GMTqap0.1-2.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/qap[mhahsler] TSP 1.2-4.1mhahsler@lyle.smu.edu (Michael Hahsler)Basic infrastructure and some algorithms for the traveling
salesperson problem (also traveling salesman problem; TSP). The
package provides some simple algorithms and an interface to the
Concorde TSP solver and its implementation of the
Chained-Lin-Kernighan heuristic. The code for Concorde itself
is not included in the package and has to be obtained
separately. Hahsler and Hornik (2007)
<doi:10.18637/jss.v023.i02>.https://github.com/r-universe/mhahsler/actions/runs/9160219041Sat, 20 Apr 2024 16:40:36 GMTTSP1.2-4.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/TSPTSP.RnwTSP.pdfIntroduction to TSP2015-10-10 03:20:132023-04-04 20:35:11[mhahsler] recommenderlab 1.0.6mhahsler@lyle.smu.edu (Michael Hahsler)Provides a research infrastructure to develop and evaluate
collaborative filtering recommender algorithms. This includes a
sparse representation for user-item matrices, many popular
algorithms, top-N recommendations, and cross-validation.
Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.https://github.com/r-universe/mhahsler/actions/runs/9533446985Mon, 18 Mar 2024 23:17:11 GMTrecommenderlab1.0.6successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/recommenderlabrecommenderlab.Rnwrecommenderlab.pdfAn introduction to the R package recommenderlab2016-05-06 21:05:292022-12-14 21:13:04[mhahsler] arules 1.7-7-1mhahsler@lyle.smu.edu (Michael Hahsler)Provides the infrastructure for representing, manipulating
and analyzing transaction data and patterns (frequent itemsets
and association rules). Also provides C implementations of the
association mining algorithms Apriori and Eclat. Hahsler, Gruen
and Hornik (2005) <doi:10.18637/jss.v014.i15>.https://github.com/r-universe/mhahsler/actions/runs/9533447217Mon, 18 Mar 2024 23:08:55 GMTarules1.7-7-1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/arulesarules.Rnwarules.pdfIntroduction to arules2015-10-12 03:57:202021-05-17 16:14:06[mhahsler] pomdpSolve 1.0.4.1mhahsler@lyle.smu.edu (Michael Hahsler)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. Kaelbling, Littman and Cassandra (1998)
<doi:10.1016/S0004-3702(98)00023-X>.https://github.com/r-universe/mhahsler/actions/runs/9170777560Thu, 01 Feb 2024 14:38:46 GMTpomdpSolve1.0.4.1successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/pomdpSolve[mhahsler] arulesSequences 0.2-30christian.buchta@wu.ac.at (Christian Buchta)Add-on for arules to handle and mine frequent sequences.
Provides interfaces to the C++ implementation of cSPADE by
Mohammed J. Zaki.https://github.com/r-universe/mhahsler/actions/runs/9233025493Thu, 25 May 2023 05:47:05 GMTarulesSequences0.2-30successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/arulesSequences[mhahsler] cba 0.2-23christian.buchta@wu.ac.at (Christian Buchta)Implements clustering techniques such as Proximus and
Rock, utility functions for efficient computation of cross
distances and data manipulation.https://github.com/r-universe/mhahsler/actions/runs/9185453635Wed, 07 Dec 2022 08:48:43 GMTcba0.2-23successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/cba[mhahsler] arulesNBMiner 0.1-8mhahsler@lyle.smu.edu (Michael Hahsler)NBMiner is an implementation of the model-based mining
algorithm for mining NB-frequent itemsets and NB-precise rules.
Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.https://github.com/r-universe/mhahsler/actions/runs/9532636541Sun, 26 Jun 2022 23:40:02 GMTarulesNBMiner0.1-8successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/arulesNBMiner[mhahsler] recommenderlabJester 0.2-0mhahsler@lyle.smu.edu (Michael Hahsler)Provides the Jester Dataset for package recommenderlab.https://github.com/r-universe/mhahsler/actions/runs/9217836973Mon, 20 Jun 2022 17:14:11 GMTrecommenderlabJester0.2-0successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/recommenderlabJester[mhahsler] recommenderlabBX 0.2-0mhahsler@lyle.smu.edu (Michael Hahsler)Provides the Book-Crossing Dataset for the package
recommenderlab.https://github.com/r-universe/mhahsler/actions/runs/9509817184Mon, 20 Jun 2022 17:13:55 GMTrecommenderlabBX0.2-0successhttps://mhahsler.r-universe.devhttps://github.com/mhahsler/recommenderlabBX