An R package for maximum likelihood parameter estimation in reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods.
Current stable resde version 1.1 on CRAN: https://cran.r-project.org/package=resde
The picture indicates growth curves that can be modelled with reducible SDEs, see here.
Get the stable version from CRAN: install.packages("resde")
or with the RStudio menus: Tools > Install Packages... > resde
Fit the Richards model dH^c = b(a^c - H^c) dt + s dW
to the heights of the first tree in Loblolly. Assume H(0) = 0, and no
observation error.
library(resde)
tree <- subset(Loblolly, Seed == Seed[1])
m <- sdemodel(~x^c, beta0=~b*a^c, beta1=~-b, mum=0)
sdefit(m, x="height", t="age", data=tree, start=c(a=70, b=0.1, c=0.5))García, O. (2019). "Estimating reducible stochastic differential equations by conversion to a least-squares problem". Computational Statistics 34(1): 23-46. doi: 10.1007/s00180-018-0837-4