Functional data can come from many different areas of study. Some of the most common examples come from finance (for example stock prices over time), or from health research (such as fMRI time series). Analyzing data of this form has been done traditionally using time series analysis techniques. However, viewing the data as functional, rather than individual observed points, can lead to more natural interpretations and analysis. Here we will be looking at a single example data set, and learning how to represent discrete data as functional data objects.
Rcpp is an R library allowing for easy integration of C++ code in your R workflow. It allows you to create optimized functions for when R just isn’t fast enough. It can also be used as a bridge between R and C++ giving you the ability to access the existing C++ libraries. Continue reading to learn how you can use Rcpp in your projects!
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Bootstrapping is a statistical technique for analyzing the distributional properties of sample data (such as variability and bias). It has many uses, and is generally quite easy to implement. Continue reading to learn how you can perform a bootstrap procedure in R!
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optimizeAPA is an R package which allows for multi-parameter optimization. That means you can use it to find the maximum (or the minimum) value of a function with many input values. What makes optimizeAPA unique? It works with arbitrary precision arithmetic.
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