Showing 7 of total 7 results (show query)
mhahsler
arules:Mining Association Rules and Frequent Itemsets
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>.
Maintained by Michael Hahsler. Last updated 2 months ago.
arulesassociation-rulesfrequent-itemsets
194 stars 13.99 score 3.3k scripts 28 dependentsmhahsler
recommenderlab:Lab for Developing and Testing Recommender Algorithms
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>.
Maintained by Michael Hahsler. Last updated 3 days ago.
collaborative-filteringrecommender-system
214 stars 10.42 score 840 scripts 2 dependentselbersb
segregation:Entropy-Based Segregation Indices
Computes segregation indices, including the Index of Dissimilarity, as well as the information-theoretic indices developed by Theil (1971) <isbn:978-0471858454>, namely the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>, is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. The package also provides a method to decompose differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>. The package includes standard error estimation by bootstrapping, which also corrects for small sample bias. The package also contains functions for visualizing segregation patterns.
Maintained by Benjamin Elbers. Last updated 1 years ago.
entropysegregationstatisticscpp
36 stars 6.44 score 51 scriptsbiorgeo
bioregion:Comparison of Bioregionalisation Methods
The main purpose of this package is to propose a transparent methodological framework to compare bioregionalisation methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).
Maintained by Maxime Lenormand. Last updated 22 days ago.
biogeographybioregionbioregionalizationcpp
7 stars 6.27 score 11 scriptsjacekbialek
PriceIndices:Calculating Bilateral and Multilateral Price Indexes
Preparing a scanner data set for price dynamics calculations (data selecting, data classification, data matching, data filtering). Computing bilateral and multilateral indexes. For details on these methods see: Diewert and Fox (2020) <doi:10.1080/07350015.2020.1816176>, Białek (2019) <doi:10.2478/jos-2019-0014> or Białek (2020) <doi:10.2478/jos-2020-0037>.
Maintained by Jacek Białek. Last updated 2 months ago.
11 stars 6.02 score 16 scriptsl-ramirez-lopez
resemble:Memory-Based Learning in Spectral Chemometrics
Functions for dissimilarity analysis and memory-based learning (MBL, a.k.a local modeling) in complex spectral data sets. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.
Maintained by Leonardo Ramirez-Lopez. Last updated 2 years ago.
chemoinformaticschemometricsinfrared-spectroscopylazy-learninglocal-regressionmachine-learningmemory-based-learningnirpedometricssoil-spectroscopyspectral-dataspectral-libraryspectroscopyopenblascppopenmp
20 stars 5.91 score 27 scriptssciviews
exploreit:Exploratory Data Analysis for 'SciViews::R'
Multivariate analysis and data exploration for the 'SciViews::R' dialect.
Maintained by Philippe Grosjean. Last updated 11 months ago.
multivariate-analysissciviewsstatistical-methods
2.70 score 4 scripts