104 skills found · Page 4 of 4
ShishirPatil / Pearson CorrelationMatlab function to generate the Pearson Product-Moment Correlation Coefficient
suyong-ustc / LuyuAnalysis of shape function under-match of digital image correlation.
VeronicaNava / RamanMPPre-processing and polymer identification of Raman spectra of plastics. Pre-processing includes normalisation functions, peak identification based on local maxima, smoothing process and removal of spectral region of no interest. Polymer identification can be performed using Pearson correlation coefficient or Euclidean distance (Renner et al. (2019), <doi:10.1016/j.trac.2018.12.004>), and the comparison can be done with a user-defined database or with the database already implemented in the package, which currently includes 356 spectra, with several spectra of plastic colorants.
lgarrison / Numba 2pcfA Numba-based two-point correlation function calculator using a grid decomposition
geophydog / ABCABC (AmBient noise and Coda) is to compute cross correlation time functions from ambient noise or coda.
lviens / 2020 ClusteringClustering algorithm to improve the retrieval of offshore-onshore correlation functions (Viens and Iwata, 2020, JGR)
HenryDuquemin / Detrended Cross Correlation Analysis DCCA Functions to implement detrended cross-correlation analysis
medhaaga / MultichainACFAuto Covariance/Correlation Function for multiple Markov chains
MarloesvL / Measure IAMeasurement code for intrinsic alignment correlation functions. For documentation see:
Wallot / MdCRQAThis repository contains R and Matlab functions for the computation of Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) from the paper "Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) – a method for quantifying correlation between multivariate time-series."
veronica-antunes / CorrLocPython tool to locate seismic signals using the cross-correlation function between station pairs
SigurdJanson / AveragingCorrelationsFunctions and analyses to illustrate the performance of correction methods that allow averaging correlation coefficients.
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.
janeminmin / Bluebikes Project1> Background information Bluebikes is Metro Boston’s public bike share program, with more than 1800 bikes at over 200 stations across Boston and nearby areas. The bikes sharing program launched in 2011. The program aimed for individuals to use it for short-term basis for a price. It allows individuals to borrow a bike from a dock station after using it, which makes it ideal for one-way trips. The City of Boston is committed to providing bike share as a part of the public transportation system. However, to build a transport system that encourages bicycling, it is important to build knowledge about the current bicycle flows, and what factors are involved in the decision-making of potential bicyclists when choosing whether to use the bicycle. It is logical to make hypotheses that age and gender, bicycle infrastructure, safety perception are possible determinants of bicycling. On the short-term perspective, it has been shown that weather plays an important role whether to choose the bicycle. 2> Data collection The Bluebikes collects and provides system data to the public. The datasets used in the project can be download through this link (https://www.bluebikes.com/system-data). Based on this time series dataset (start from 2017-01-01 00:00:00 to 2019-03-31 23:00:00), we could have the information includes: Trip duration, start time and data, stop time and data, start station name and id, end station name and id, bike id, user type (casual or subscribed), birth year, gender. Besides, any trips that were below 60 seconds in length is considered as potentially false starts, which is already removed in the datasets. The number of bicycles used during a particular time period, varies over time based on several factors, including the current weather conditions, time of the day, time of the year and the current interest of the biker to use the bicycle as a transport mode. The current interest is different between subscribed users and casual users, so we should analyze them separately. Factors such as season, day of a week, month, hour, and if a holiday can be extracted from the date and time column in the datasets. Since we would analyze the hourly bicycle rental flow, we need hourly weather conditions data from 2017-01-01 00:00:00 to 2019-03-31 23:00:00 to complete our regression model of prediction. The weather data used in the project is scrapped using python selenium from Logan airport station (42.38 °N, 71.04 °W) webpage (https://www.wunderground.com/history/daily/us/ma/boston/KBOS/date/2019-7-15) maintained by weather underground website. The hourly weather observations include time, temperature, dew point, humidity, wind, wind speed, wind gust, pressure, precipitation, precipitation accumulated, condition. 3> The problem The aims of the project are to gain insight of the factors that could give short-term perspective of bicycle flows in Boston. It also aimed to investigate the how busy each station is, the division of bicycle trip direction and duration of the usage of a busy station and the mean flows variation within a day or during that period. The addition to the factors included in the regression model, there also exist other factors than influence how the bicycle flows vary over longer periods time. For example, general tendency to use the bicycle. Therefore, there is potential to improve the regression model accuracy by incorporating a long-term trend estimate taken over the time series of bicycle usage. Then the result from the machine learning algorithm-based regression model should be compared with the time series forecasting-based models. 4> Possible solutions Data preprocessing/Exploration and variable selection: date approximation manipulation, correlation analysis among variables, merging data, scrubbing for duplicate data, verifying errors, interpolation for missing values, handling outliers and skewness, binning low frequent levels, encoding categorical variables. Data visualization: split number of bike usage by subscribed/casual to build time series; build heatmap to present how busy is each station and locate the busiest station in the busiest period of a busy day; using boxplot and histogram to check outliers and determine appropriate data transformation, using weather condition text to build word cloud. Time series trend curve estimates: two possible way we considered are fitting polynomials of various degrees to the data points in the time series or by using time series decomposition functions and forecast functions to extract and forecast. We would emphasize on the importance to generate trend curve estimates that do not follow the seasonal variations: the seasonal variations should be captured explicitly by the input weather related variables in the regression model. Prediction/regression/time series forecasting: It is possible to build up multilayer perceptron neural network regressor to build up models and give prediction based on all variables of data, time and weather. However, considering the interpretability of model, we prefer to build regression models based on machine learning algorithms (like random forest or SVM) respectively for subscribed/casual users. Then the regressor would be combined with trend curve extracted and forecasted by ARIMA, and then comparing with the result of time series forecasting by STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods and the result of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA residuals, Trend and Seasonality).
PFLeget / TreegpGaussian Processes using information from the 2-point correlation function and mean function
INM-6 / Correlation ToolboxCollection of functions to investigate correlations in spike trains and membrane potentials
growlix / Morans IMATLAB Functions for computing two measures of spatial correlation: Moran's I, a measure of spatial autocorrelation, and the Mantel test, a correlation between physical distance and feature distance
rnuske / ApcfR Package providing the Adapted Pair Correlation Function
ggscorzato / MCfitsMatlab functions to extract basic physical quantities from correlation functions in twisted mass Lattice QCD
jrjohansson / QdpackQuantum Dynamics package. A numerical package for simulation of quantum system (unitary evolution, dissipative evolution, steady state, expectation values and correlation functions, etc).
