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rkillick
changepoint:Methods for Changepoint Detection
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Maintained by Rebecca Killick. Last updated 4 months ago.
134 stars 11.05 score 736 scripts 42 dependents
rsquaredacademy
rfm:Recency, Frequency and Monetary Value Analysis
Tools for RFM (recency, frequency and monetary value) analysis. Generate RFM score from both transaction and customer level data. Visualize the relationship between recency, frequency and monetary value using heatmap, histograms, bar charts and scatter plots. Includes a 'shiny' app for interactive segmentation. References: i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.
Maintained by Aravind Hebbali. Last updated 1 years ago.
customer-analyticscustomer-segmentationrfm-analysissegmentation
61 stars 6.68 score 94 scripts
euanmcgonigle
CptNonPar:Nonparametric Change Point Detection for Multivariate Time Series
Implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2023) <doi:10.48550/arXiv.2305.07581> for description of the NP-MOJO methodology.
Maintained by Euan T. McGonigle. Last updated 11 months ago.
change-point-detectionmoving-sumnonparametricsegmentationtime-seriescpp
4 stars 3.60 score 4 scripts