Statsmodels: statistical modeling and econometrics in Python
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Updated
Jun 1, 2024 - Python
Statsmodels: statistical modeling and econometrics in Python
Flexible Statistics and Data Analysis (FSDA) extends MATLAB for a robust analysis of data sets affected by different sources of heterogeneity. It is open source software licensed under the European Union Public Licence (EUPL). FSDA is a joint project by the University of Parma and the Joint Research Centre of the European Commission.
Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
Certifiable Outlier-Robust Geometric Perception
This library is an implementation of the algorithm described in Distributed Trajectory Estimation with Privacy and Communication Constraints: a Two-Stage Distributed Gauss-Seidel Approach.
Code for "EPOS: Estimating 6D Pose of Objects with Symmetries", CVPR 2020.
Mean and Covariance Matrix Estimation under Heavy Tails
Solve many kinds of least-squares and matrix-recovery problems
Code/Examples for A New Approach to Robust Estimation of Parametric Structures
Robust estimations from distribution structures: Mean.
A new take on EEG sleep spindles detection exploiting a generative model (dynamic bayesian network) to characterize reoccurring dynamical regimes of single-channel EEG.
(ICML 2020) Message Passing Least Squares Algorithm for Rotation Averaging
MATLAB demo for the paper "Non-smooth M-Estimator for Maximum Consensus Estimation"
Robust estimations from distribution structures: Invariant moments.
Stata package for robust stochastic frontier analysis
Robust estimations from distribution structures: Central moments.
FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well in noisy and contaminated datasets.
FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well in noisy and contaminated datasets.
Official implementation of the paper: "REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions", IEEE WACV, 2022
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