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Package for Analysis of Space-Time Ecological Series
Regularisation, decomposition and analysis of space-time series.
The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff
Unit Root Tests for Seasonal Time Series
Seasonal unit roots and seasonal stability tests. P-values based on response surface regressions are available for both tests. P-values based on bootstrap are available for seasonal unit root tests.
'NetCDF' Geometry and Time Series
Tools to create time series and geometry 'NetCDF' files.
Nonlinear Time Series Analysis
R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.
Analyze Paleontological Time-Series
Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).
Methods for Handling and Analyzing Time Series of Satellite Images
Provides functions and methods for: splitting large raster objects
into smaller chunks, transferring images from a binary format into raster
layers, transferring raster layers into an 'RData' file, calculating the
maximum gap (amount of consecutive missing values) of a numeric vector,
and fitting harmonic regression models to periodic time series. The homoscedastic
harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000)
Interpreting Time Series and Autocorrelated Data Using GAMMs
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms
Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.
Robust Covariance Matrix Estimators
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic
robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
to other classes through S3 methods. Details can be found in Zeileis et al. (2020)
Resampling Tools for Time Series Forecasting
A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.