R-universe search: topic:dynamic-programming

Showing 3 of total 3 results (show query)

fchamroukhi

samurais:Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references. These models are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?&tab=repositories&q=time-series&type=public&language=matlab>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence change-point-detection data-science dynamic-programming em-algorithm hidden-markov-models hidden-process-regression human-activity-recognition latent-variable-models model-selection multivariate-timeseries newton-raphson piecewise-regression statistical-inference statistical-learning time-series-analysis time-series-clustering openblas cpp

11 stars 6.14 score 28 scripts

fchamroukhi

flamingos:Functional Latent Data Models for Clustering Heterogeneous Curves ('FLaMingos')

Provides a variety of original and flexible user-friendly statistical latent variable models for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time series, or more generally longitudinal data, fitted by unsupervised algorithms, including EM algorithms. Functional Latent Data Models for Clustering heterogeneous curves ('FLaMingos') are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. The references are mainly the following ones. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>. Chamroukhi F., Same A., Aknin P. and Govaert G. (2011) <doi:10.1109/IJCNN.2011.6033590>. Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>. Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>. Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence baum-welch-algorithm curve-clustering data-science dynamic-programming em-algorithm functional-data-analysis functional-data-clustering hidden-markov-models hidden-process-regression mixture-models piecewise-regression statistical-analysis statistical-inference statistical-learning time-series-analysis unsupervised-learning openblas cpp

6 stars 4.95 score 9 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.

changepoint dynamic-programming lasso neuroscience statistics

8 stars 3.60 score 8 scripts