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Dynamic Linear Models with R (Use R) Giovanni Petris, Sonia Petrone, Patrizia Campagnoli
Publisher: Springer

Below is a simple plot of a kalman filtered version of a random walk (for now, we will use that as an estimate of a financial time series). In addition, there is a kalman smoother in the R package, DLM. The distinction between Toolboxes and Utilities can be blurry, but for the purposes of this page we define a toolbox to be a utility that can be completely operated via a graphical user interface. If the proportion data do not arise from a binomial process (e.g., proportion of a leaf consumed by a caterpillar), then . Among other things, they've developed a very handy-looking R package called mosaic, which simplifies the use of R for basic statistical and modeling task, and alters the output in a way designed to be friendly and people new to both . Dynamic Bradley-Terry modelling of sports tournaments. CGIwithR: Facilities for processing web forms using R. An Assessment of Frameworks Useful for Public Land Recreation Planning. Based on general linear model and Sun's tube formula, NIRS-SPM not only provides activation maps of oxy-, deoxy-, and total- hemoglobin, but also allows for the super-resolution activation localization. The general approach is to tell R to exclude one or both of the axes when drawing the plot and then use the axis( ) function to customize the axes by telling R which labels to use and where to put them. It also allows the user to specify a general model, for example, a quadratic model, with constant and quadratic terms, but no linear term. Although R has many flaws, it is well suited to programming with data, and has a huge array of statistical libraries associated with it. Unlike a simple moving of the kalman filter. The package provides a simple inline interface to Stan which takes BUGS like code, translates it into C++, compiles and loads the dynamic library into R and runs your MCMC for you (phew!) (BTW: The guts are based on the inline, What's more relevant for applied researchers like me is that the algorithms used are cutting edge and use modified HMC coupled with Automatic Differentiation to achieve rather quick mixing. Journal of Statistical Software 8(10), 1–8. Kalman Filter estimates of mean and covariance of Random Walk The kf is a fantastic example of an adaptive model, more specifically, a dynamic linear model, that is able to adapt to an ever changing environment. This could be time efficient, as the debugging and re-factoring can take place in the dynamic language where it is easier, then just re-coded fairly directly into the statically typed language once the code is working well. M, Varin, C and Firth, D (2013). Modelling subjective use of an ordinal response scale in a many period crossover experiment. Applied Statistics 51, 245–255. Like many statisticians, I probably use R more than any other language in my day-to-day work. On the index of dissimilarity for lack of fit in log-linear and log-multiplicative models. Essentially, the higher R^2 is, the lower the t-values will be. Computational Statistics and Data Firth, D. As a general rule, you should not transform your data to try to fit a linear model.