R-universe search: topic:statistical-learning

Showing 11 of total 11 results (show query)

giscience-fsu

sperrorest:Perform Spatial Error Estimation and Variable Importance Assessment

Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.

Maintained by Alexander Brenning. Last updated 2 years ago.

cross-validation machine-learning spatial-statistics spatio-temporal-modeling statistical-learning

19 stars 6.46 score 46 scripts

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

techtonique

learningmachine:Machine Learning with Explanations and Uncertainty Quantification

Regression-based Machine Learning with explanations and uncertainty quantification.

Maintained by T. Moudiki. Last updated 4 months ago.

conformal-prediction machine-learning machine-learning-algorithms machinelearning statistical-learning uncertainty-quantification cpp

5 stars 5.53 score 21 scripts

fchamroukhi

meteorits:Mixture-of-Experts Modeling for Complex Non-Normal Distributions

Provides a unified mixture-of-experts (ME) modeling and estimation framework with several original and flexible ME models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according to non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. Mixtures-of-Experts models for complex and non-normal distributions ('meteorits') are originally introduced and written in 'Matlab' by Faicel Chamroukhi. The references are mainly the following ones. The references are mainly the following ones. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F. (2015) <arXiv:1506.06707>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>. Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>. Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence clustering em-algorithm mixture-of-experts neural-networks non-linear-regression prediction robust-learning skew-normal skew-t skewed-data statistical-inference statistical-learning t-distribution unsupervised-learning openblas cpp

3 stars 5.12 score 11 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

techtonique

ahead:Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Maintained by T. Moudiki. Last updated 1 months ago.

forecasting machine-learning predictive-modeling statistical-learning time-series time-series-forecasting uncertainty-quantification cpp

21 stars 4.63 score 51 scripts

clement-w

SIRthresholded:Sliced Inverse Regression with Thresholding

Implements a thresholded version of the Sliced Inverse Regression method, which allows to do variable selection.

Maintained by Clement Weinreich. Last updated 6 months ago.

dimensionality-reduction inverse-regression statistical-learning variable-selection

4 stars 4.30 score 4 scripts

techtonique

bcn:Boosted Configuration Networks

Boosted Configuration (neural) Networks for supervised learning.

Maintained by T. Moudiki. Last updated 6 months ago.

machine-learning neural-networks statistical-learning cpp

5 stars 4.00 score 4 scripts

njtierney

broomstick:Convert Decision Tree Objects into Tidy Data Frames

Convert Decision Tree objects into tidy data frames, by using the framework laid out by the package broom, this means that decision tree output can be easily reshaped, porocessed, and combined with tools like 'dplyr', 'tidyr' and 'ggplot2'. Like the package broom, broomstick provides three S3 generics: tidy, to summarise decision tree specific features - tidy returns the variable importance table; augment adds columns to the original data such as predictions and residuals; and glance, which provides a one-row summary of model-level statistics.

Maintained by Nicholas Tierney. Last updated 1 years ago.

broom decision-trees gbm machine-learning randomforest rpart statistical-learning

29 stars 3.59 score 27 scripts

yuting1214

TensorTest2D:Fitting Second-Order Tensor Data

An implementation of fitting generalized linear models on second-order tensor type data. The functions within this package mainly focus on parameter estimation, including parameter coefficients and standard deviation.

Maintained by Mark Chen. Last updated 8 months ago.

statistical-learning tensor

1 stars 2.70 score 5 scripts

techtonique

nnetsauce:Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Maintained by T. Moudiki. Last updated 7 months ago.

deep-learning machine-learning neural-networks randomized-algorithms statistical-learning

2 stars 2.60 score 6 scripts