SemNetCleaner - An Automated Cleaning Tool for Semantic and Linguistic Data
Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.
Last updated 2 years ago
preprocessingsemantic-network-analysis
6.06 score 8 stars 1 packages 48 scripts 456 downloadsSemNetDictionaries - Dictionaries for the 'SemNetCleaner' Package
Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to <https://github.com/AlexChristensen/SemNetDictionaries>.
Last updated 3 years ago
dictionariessemantic-network-analysis
5.08 score 4 stars 2 packages 3 scripts 462 downloadsSemNeT - Methods and Measures for Semantic Network Analysis
Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.
Last updated 1 years ago
semantic-network-analysis
4.47 score 21 stars 28 scripts 342 downloadslatentFactoR - Data Simulation Based on Latent Factors
Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Last updated 3 months ago
4.02 score 3 stars 2 scripts 551 downloads