21 skills found
firmai / Mtss GanMTSS-GAN: Multivariate Time Series Simulation with Generative Adversarial Networks (by @firmai)
brunzema / Truncated Mvn SamplerReimplementation using Python of the minimax tilting algorithm by Botev (2016) for simulation and iid sampling of the truncated multivariate normal distribution.
sccn / SIFTSIFT is an EEGLAB-compatible toolbox for analysis and visualization of multivariate causality and information flow between sources of electrophysiological (EEG/ECoG/MEG) activity. It consists of a suite of command-line functions with an integrated Graphical User Interface for easy access to multiple features. There are currently six modules: data preprocessing, model fitting and connectivity estimation, statistical analysis, visualization, group analysis, and neuronal data simulation.
KeckCAVES / 3DVisualizerHighly interactive application for visualization and analysis of 3D multivariate gridded data, such as produced by finite element method (FEM) simulations, confocal microscopy, serial sectioning, computerized axial tomography (CAT) scans, or magnetic resonance imaging (MRI) scans
zoj613 / HtnormFast and Exact Simulation of Hyperplane-Truncated Multivariate Normal Distributions, with C, Python and R interfaces.
cehrett / Subset Simulation With Multivariate DrawSubset simulation is a method of estimating low probability events. Here I adapt SS to perform well with correlated inputs.
ragoragino / Py HawkesPython library with C++ extensions for simulation, compensator, log-likelihood and intensity function computation for a multivariate Hawkes processes.
itsoukal / AnySimAn R package for the stochastic simulation of processes with any marginal distribution and correlation structure
n1tk / ResearchLSTMMultivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model
ybcmath / MultiSTHPMultivariate Spatiotemporal Hawkes Processes Simulation and Network Reconstruction
sjiggins / Carl TorchPytorch toolbox for multivariate reweighting of Monte Carlo simulation.
RikHenson / MultivarConMultivariate connectivity analysis simulations for fMRI/MEEG
pizhn / Marked MHPMarked-multivariate Hawkes Process simulation and estimation
leandrofgr / DMSDirect Multivariate Simulation
barbarabodinier / FakeR package fake (Flexible Data Simulation Using The Multivariate Normal Distribution).
mosesyhc / LCGPLatent component Gaussian process, an emulator strategy for multivariate stochastic simulations.
NabarunD / MultiDistFreeThis repository consists of codes used in simulations corresponding to nonparametric multivariate distribution-free ests
slerch / Multiv PpCode for paper on simulation-based comparison of multivariate ensemble post-processing methods
chaohstat / FGWASFGWAS (Functional Genome Wide Association analysiS) is a Python coding based package for imaging genetic analysis. Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this toolbox is to develop a functional genome-wide association analysis framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genomewide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate.
ellenicoleroberts / Modeling The VIX With LSTMVarious multivariate, multistep LSTM models using SPX options data to forecast the CBOE VIX to improve future market volatility forecasts. Monte Carlo simulation and Facebook Prophet forecasts included for comparison.
