25 skills found
deepcausality-rs / Deep CausalityHyper-geometric computational causality for Rust
mrosol / NonlincausalityPython package for Granger causality test with nonlinear forecasting methods.
semvijverberg / RGCPDClimate analysis toolbox to investigate teleconnections, test for causality, and make forecasts.
py-why / Pywhy StatsPython package for (conditional) independence testing and statistical functions related to causality.
asymppdc / AsympPDCThe asympPDC Package is a MATLAB and Octave package for Partial Directed Coherence (PDC) and Directed Transfer Function (DTF) estimation with asymptotic statistics, allied functions and routines for Granger Causality Test and results pretty plotting.
Marga8 / HDGCvarGranger causality testing in High Dimensional Vector Autoregressive Models
Dan-Boat / PyClimatThis repository contains scripts for Python Module for Climate Data Analysis and Visualization, which is under development. The module performs many climate data analysis like correlation, significance testing, causality testing, estimation of climatologies, etc. The current version of package can be used, however, it must be handle with caution since most of the functions are yet to be tested. Kindly contact me for help if required.
brian-lau / GrangerMatlab code for frequency-domain Granger causality with significance testing
fischJan / CiRASystem behavior is often expressed by causal relations in requirements (e.g. if event 1 then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language (NL) is still an open research challenge as existing approaches fail to extract causality with reasonable performance.
Jenny1228 / Analysis On Relation Between Real Time News Sentiment And Stock Market PortfolioIn this project, we use two sets of data to draw insights on how media sentiment can be an indicator for the financial sector. For the financial data, we plan to use daily return of the market index <font color='green'>(^GSPC)</font>, which is a good indicator for market fluctuation; for media sentiment, we use summarized information of news pieces from top 10 most popular press because of their stronger influence in shaping people's perception of events that are happening in the world.** **Both sets of data are real-time, which means the source files are of the moment and need to be loaded each time analysis is performed. The sentiment analysis library returns a <font color='green'>polarity</font> score (-1.0 to 1.0) and a <font color='green'>polarity</font> score (0.0 to 1.0) on the news stories. Using quantified sentiment analysis, we juxtapose the two time series of data and observe if they present any correlation and search for potential causality. For example, we may test the hypothesis that when polarity among the daily news posts is higher (a.k.a., positive), the financial market that same day is more likely to rise. The rest of the notebook is a step-by-step instruction.
firoozye / Crypto CausalityExplore Crypto Causality using variety of Granger tests, for possible adaptive filtering
QuantLet / GrangerCausalityTestInQuantileNo description available
nicolarighetti / Granger Causality Test With RFour ways to perform Granger Causality test with R
Koni2020 / PyLiangA causality test in climate
Marga8 / Granger Causality In High Dimensional VARsThis repo gather R functions to reproduce analyses on the paper: Hecq,A.,Margaritella,L.,Smeekes,S. (2019), "Granger Causality testing in High-Dimensional VARs: a Post-Double-Selection Procedure"
leelew / RFGrangerNonlinear Granger causality test based on random forest (source code of "A Causal Inference Model based on Random Forest to identify soil moisture-precipitation feedback", Journal of Hydrometeorology)
BethanyL / GcSuite of tests for network inference methods such as Granger causality
nicolarighetti / Toda Yamamoto Causality TestR function to perform the Toda-Yamamoto causality test
jeckonov / EconometricsFilters (kalman, hodrick-prescott, moving average) together with comparison and sensitivity analysis (in notebook filters_with_parameters)+var analysis and granger causality test. Test for random walk (CE currencies using yfinance API)
MatFar88 / GrangersThis repository contains data and code relative to the manuscript "A bootstrap test to detect prominent Granger-causalities across frequencies" by Matteo Farnè and Angela Montanari.
