meandr allows for easy generation of coordinates that are random, yet continuously differentiable. This is particularly useful for simulating time-series measurements of physical phenomena that maintain a clear local trajectory.

## Installation

devtools::install_github("sccmckenzie/meandr")

## Why meandr?

Suppose we want to simulate behavior of a “somewhat random” time-series phenomenon.

• Outdoor temperature
• Train station crowd density
• Stock price

Although we can’t predict the exact values of these examples, we know how they will behave to a certain extent. For instance, outdoor temperature is not going to drop by 100 degrees in 1 second.

We could use method #1 below:

method_1 <- data.frame(t = 1:100,
f = rnorm(100))

The above data doesn’t exhibit any prolonged directional consistency. This may not adequately emulate the character of the above examples.

meandr offers a solution to this problem. Each call to meandr() generates a unique tibble of t and f coordinates. For reproducibility, a seed argument is provided.

library(meandr)

df1 <- meandr(n_points = 100,
n_nodes = 20,
seed = 2)

df1
#> # A tibble: 100 x 2
#>         t        f
#>     <dbl>    <dbl>
#>  1 0.01   -0.00400
#>  2 0.02   -0.0160
#>  3 0.03   -0.0360
#>  4 0.04   -0.0640
#>  5 0.05   -0.100
#>  6 0.06   -0.144
#>  7 0.0700 -0.196
#>  8 0.08   -0.256
#>  9 0.09   -0.324
#> 10 0.10   -0.400
#> # ... with 90 more rows

Observe df1 curve trajectory never radically changes between two points. This is a key feature of meandr: all curves are continuously differentiable.