
distantia - Advanced Toolset for Efficient Time Series Dissimilarity Analysis
Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.
Last updated 1 months ago
dissimilaritydynamic-time-warpinglock-steptime-seriescpp
5.73 score 23 stars 11 scripts 325 downloads
collinear - Automated Multicollinearity Management
Effortless multicollinearity management in data frames with both numeric and categorical variables for statistical and machine learning applications. The package simplifies multicollinearity analysis by combining four robust methods: 1) target encoding for categorical variables (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); 2) automated feature prioritization to prevent key variable loss during filtering; 3) pairwise correlation for all variable combinations (numeric-numeric, numeric-categorical, categorical-categorical); and 4) fast computation of variance inflation factors.
Last updated 3 months ago
machine-learningmulticollinearitystatistics
5.51 score 11 stars 1 dependents 15 scripts 737 downloadsvirtualPollen - Simulating Pollen Curves from Virtual Taxa with Different Life and Niche Traits
Tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples.
Last updated 3 years ago
4.40 score 5 stars 5 scripts 152 downloads