Showing 6 of total 6 results (show query)
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
weecology
LDATS:Latent Dirichlet Allocation Coupled with Time Series Analyses
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
Maintained by Juniper L. Simonis. Last updated 5 years ago.
changepointldaparallel-temperingportalsoftmax
25 stars 6.93 score 45 scripts
jongheepark
NetworkChange:Bayesian Package for Network Changepoint Analysis
Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
Maintained by Jong Hee Park. Last updated 3 years ago.
bayesianchangepointlatent-spacenetwork
5 stars 4.60 score 16 scripts
jewellsean
LZeroSpikeInference:Exact Spike Train Inference via L0 Optimization
An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.
Maintained by Sean Jewell. Last updated 6 years ago.
changepointdynamic-programminglassoneurosciencestatistics
8 stars 3.60 score 8 scripts
shinjaehyeok
stcpR6:Sequential Test and Change-Point Detection Algorithms Based on E-Values / E-Detectors
Algorithms of nonparametric sequential test and online change-point detection for streams of univariate (sub-)Gaussian, binary, and bounded random variables, introduced in following publications - Shin et al. (2024) <doi:10.48550/arXiv.2203.03532>, Shin et al. (2021) <doi:10.48550/arXiv.2010.08082>.
Maintained by Jaehyeok Shin. Last updated 5 months ago.
changepointsequential-testingtime-series-analysiscpp
3.52 score 11 scripts
jrjthompson
nonsmooth:Nonparametric Methods for Smoothing Nonsmooth Data
Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) <doi:10.1080/02664763.2024.2352759>.
Maintained by John R.J. Thompson. Last updated 9 months ago.
change-pointchange-point-modelingchangepointkernelkernel-methodsnonparametric-regressionsmoothingsmoothing-methods
2.48 score