SemNeT 2.0.0

How To Install

if(!"devtools" %in% row.names(installed.packages())){
  install.packages("devtools")
}

devtools::install_github("AlexChristensen/SemNeT", dependencies = c("Imports", "Suggests"))

How To Use

Christensen, A. P., & Kenett, Y. N. (in press). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods. https://doi.org/10.1037/met0000463 (see PsyArXiv for preprint)

SemNeT

SemNeT offers researchers several tools for the analysis of their semantic network data. As a part of a module of semantic network packages, SemNeT is the most general, providing statistical analyses for all types of semantic networks.

Version 2.0.0: A Rebuilt Package

Version 2.0.0 is a from-scratch rebuild of the package's internals, aimed at long-term reliability rather than new features. Highlights:

• One function, one file. Every exported function (e.g. tmfg(), bootstrap_SemNeT()) now lives in its own file alongside its own input validation, instead of being scattered across large, multi-purpose scripts.
• Real bugs fixed. Several silent correctness issues in the pre-2.0.0 codebase are fixed in this version -- inconsistent equating logic, a few reversed/dropped loop ranges on degenerate inputs, a signed-weight NaN in weighted clustering coefficient, duplicate-name handling across every function that takes named ... groups, and more. See NEWS.md for the full list.
• Old code keeps working. Every pre-2.0.0 exported name and call style (e.g. bootSemNeT(), TMFG(), test.bootSemNeT()) still works, as a thin wrapper around its new-named replacement (e.g. bootstrap_SemNeT(), tmfg(), bootstrap_test_SemNeT()). Existing scripts do not need to change.
• Fewer dependencies. Plotting and parallelism no longer pull in ggplot2/dplyr/plyr/magrittr/pbapply/philentropy; network plotting now goes through base graphics and future/future.apply handles parallel bootstrapping with guaranteed cleanup.
• Permutation testing, exported for the first time. permutation_SemNeT() (formerly an internal, undocumented permSemNeT()) is now a fully validated, documented, exported function.

The Shiny app (SemNeTShiny()) is not yet part of this rebuild and remains on the pre-2.0.0 code path for now.

Forward Flow

Implements the forward flow measure introduced by Gray et al. (2019) and semantic spaces from Beaty et al. (2021).

SemNeT Shiny

From raw data to semantic network analysis in three lines of code: The Shiny app allows for integration with SemNetCleaner, streamlining the SemNA pipeline:

# Grouping variable
group <- SemNeT::open.group

# Preprocessed data
clean <- SemNetCleaner::textcleaner(
    data = SemNetCleaner::open.animals[,-c(1,2)], type = "fluency",
    miss = 99, partBY = "row", dictionary = "animals"
)

# SemNeT Shiny app for network estimation and analyses
SemNeT::SemNeTShiny()

The point and click interface of the SemNeT Shiny app enables users to perform all analyses in the package as well as spreading activation analyses from the spreadr package (Siew, 2019).

References

Beaty, R. E., Zeitlen, D. C., Baker, B. S., & Kenett, Y. N. (2021). Forward flow and creative thought: Assessing associative cognition and its role in divergent thinking. Thinking Skills and Creativity, 100859. https://doi.org/10.1016/j.tsc.2021.100859

Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32(4), 480-492. https://doi.org/10.1002/per.2157

Gray, K., Anderson, S., Chen, E. E., Kelly, J. M., Christian, M. S., Patrick, J., ... & Lewis, K. (2019). “Forward flow”: A new measure to quantify free thought and predict creativity. American Psychologist, 74(5), 539-554. https://doi.org/10.1037/amp0000391

Kenett, Y. N., & Austerweil, J. L. (2016). Examining search processes in low and high creative individuals with random walks. In Paper presented at the proceedings of the 38th annual meeting of the cognitive sceince society (pp. 313-318). Austin, TX. Retrieved from https://cogsci.mindmodeling.org/2016/papers/0066/index.html

Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8, 407. https://doi.org/10.3389/fnhum.2014.00407

Kenett, Y. N., Wechsler-Kashi, D., Kenett, D. Y., Schwartz, R. G., Ben Jacob, E., & Faust, M. (2013). Semantic organization in children with cochlear implants: Computational analysis of verbal fluency. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00543

Siew, C. S. Q. (2019). spreadr: An R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. https://doi.org/10.3758/s13428-018-1186-5