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Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
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)
Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy
Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013)
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.
Forecasting Time Series by Theta Models
Routines for forecasting univariate time series using Theta Models.
Smoothing Long-Memory Time Series
The nonparametric trend and its derivatives in equidistant time
series (TS) with long-memory errors can be estimated. The
estimation is conducted via local polynomial regression using an
automatically selected bandwidth obtained by a built-in iterative plug-in
algorithm or a bandwidth fixed by the user.
The smoothing methods of the package are described in Letmathe, S., Beran,
J. and Feng, Y., (2023)
Multivariate Time Series Data Imputation
This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
Inferring Causal Effects using Bayesian Structural Time-Series Models
Implements a Bayesian approach to causal impact estimation in time
series, as described in Brodersen et al. (2015)
Time Value of Money, Time Series Analysis and Computational Finance
Package for time value of money calculation, time series analysis and computational finance.
Superfast Likelihood Inference for Stationary Gaussian Time Series
Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.