Why do causal mapping rather than ordinary QDA?
I've got a load of texts to analyse, can I use Causal Map just for standard Qualitative Data Analysis, like ordinary thematic analysis, to identify important themes?
Open-source causal mapping functions
Over the last three years we have developed a whole range of algorithms for causal mapping. The algorithms are published open-source in the form of functions for the programming language R.
These functions are the heart of the analysis engine in our app CausalMap3. The app itself is free to use for analysing existing causal mapping data files but it is closed source, so the functions are a reproducible window into the algorithms and can be used by anyone who wants to analyse and visualise causal mapping data without using the Causal Map app.
When we get a chance, we will release them as a full R package. But for now you can read and download a single R script which contains the functions and all you need to use them.
We are still in the process of documenting the functions. To make them easier to use, we have a vignette which demonstrates the use of many of the functions as applied to example causal mapping data files (here is the main example file and here is a special example to demonstrate the use of combined opposites).
The vignette is an .Rmd file: if you process it using the package knitr or by pressing Knit in Rstudio, you should get output like this. It contains over 50 different types of map.
