NEWS.md

In datafsm: Estimating Finite State Machine Models from Data

datafsm 0.2.4

• Fix error in testing where I created a matrix from a vector of length 8 specifying 2 columns and 2 rows.
• Fix obsolete URL http://www.jstatsoft.org to https://www.jstatsoft.org.
• Use new \doi tag for sources in Rd documentation where the URL pointed to https://doi.org/.

datafsm 0.2.3

• Replace dependency on the tidyverse package with dependencies on the specific packages from tidyverse: dplyr, tidyr, and purrr.

datafsm 0.2.2

• Rewrote covariate testing in fitnessCPP to fix memory access errors.
• Added new vignette, giving an example using real data from Fudenberg, Rand, and Dreber.
• Change progress reports to use message() and warning() instead of print() and cat()
• Added new data set with iterated prisoner's dilemma games from Nay and Vorobeychik (2016).

datafsm 0.2.1

• Updated tests to work with forthcoming testthat version 2.0.0

datafsm 0.2.0

• Updated to use new R native-routine registration and disable symbol search using Rcpp::compileAttributes().

datafsm 0.1.2

• Fixed line dropping NZV vars (line 211 of evolve_model.R)
• Changed all occurrences of maxfitness to maxFitness to comply with new GA version soon to be on CRAN.

datafsm 0.1.1

Second release of package.

• Updated vignette to properly simulate tit-for-tat data, and set a seed. Added FRD data vignette to show more advanced use of package on real data.
• Changed stop to warning if length(names) > 3. Added some text to stop() for ncol(data) != inputs.
• Made var_imp more modular.
• Took out core computation and put it in its own function.
• Added a var_imp2() function that uses output of this new importance() function and returns results for every element of state matrix, not just the colSums and puts that in a new slot in the main object called varImp2 var_imp2() returns raw performance scores.
• Added documentation for varImp2. Added check for another (4th) covariate value in C++.
• Test for main function now expects a warning rather than an error for when we have more than 3 predictors.
• Biggest change: Added evolve_model_ntimes() function to run evolve_model() n times and return either the best or all of them, depending on user specification.
• In evolve_model() instead of missing(), now use is.null() so evolve_model can be called inside other functions easily.

datafsm 0.1

First release of package.

datafsm documentation built on May 30, 2021, 1:06 a.m.