An application of Dynamic Mode Decomposition for prediction of time features. Automatic search for the best model across the space of all possible feature combinations and ranks of Singular Value Decomposition.

Time series regression.  The dyn class interfaces ts,
        irts(), zoo() and zooreg() time series classes to lm(), glm(),
        loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(),
        quantreg::rq(), randomForest::randomForest() and other regression
        functions allowing those functions to be used with time series
        including specifications that may contain lags, diffs and
        missing values.

A comprehensive and dynamic configuration driven logging package for R. While there 
    are several excellent logging solutions already in the R ecosystem, I always feel constrained 
    in some way by each of them. Every project is designed differently to solve it's domain specific problem, 
    and ultimately the utility of a logging solution is its ability to adapt to this design. This is the 
    raison d'être for 'dyn.log': to provide a modular design, template mechanics and a configuration-based 
    integration model, so that the logger can integrate deeply into your design, even though it knows 
    nothing about it.

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Generalised model for population dynamics of invasive Aedes mosquitoes. Rationale and model structure are described here: Da Re et al. (2021) <doi:10.1016/j.ecoinf.2020.101180> and Da Re et al. (2022) <doi:10.1101/2021.12.21.473628>.