Finds regular and chaotic intervals in the data using 
    the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) 
	<DOI:10.1137/080718851>.

An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.

Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.

Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.

Mechanistically models/predicts the phenology (macro-phases) of 10 crop plants (trained on a big dataset over 80 years derived from the German weather service (DWD) <https://opendata.dwd.de/>). Can be applied for remote sensing purposes, dynamically check the best subset of available covariates for the given dataset and crop.

The DYMO package provides tools for multi-feature time-series forecasting using a Dynamic Mode Decomposition (DMD) model combined with conformal predictive sampling for uncertainty quantification.

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.