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citelens is on CRAN

I'm happy to share that  citelens  0.1.0 is now available on  CRAN .  What it does citelens  turns the citations you already export into something you can analyze. It converts RefWorks exports to BibTeX, loads BibTeX into a data frame, and exports the results to Excel. It also reads saved Google Scholar pages and can look up missing abstracts through OpenAlex. Once the abstracts are in, it can analyze them with topic modeling (LDA), document similarity, and trends over time, with results returned as charts and data frames. The conversion code is written in C++ via Rcpp, so large reference libraries convert quickly.  Fig 1. Topic trends over time. Install install.packages("citelens") The development version is on  GitHub : remotes::install_github("cownr10r/citelens") Fig 2. Word trends over time. Why I built it I built citelens for researchers who want to explore the literature on a topic but work somewhere that doesn’t provide Web of Science or Scopus,...
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The "Cram" Method of Validation for Machine learning

Our department at the University of Houston has a paper that is accepted and will be presented at the 2026 conference of the American Educational Research Association (AERA) in Los Angeles, CA. Without getting too deep into the purpose of the paper, while simultaneously aiming for the purpose of this writing, which is showcasing our use of novel methodology in action, we used machine learning ML algorithms to make our case. As a result, we needed to validate the machine learning outcomes. Traditionally this is done with an 80/20 holdout reservation of part of the data. Indeed, one of our critics asked us to use a holdout method in a particular way. However, as a data scientist, I am always championing the newest methodologies out there available in the R coding sphere (read as a linguistic sphere altogether). As I was looking out during the break for newest releases, I came across the work of Jia, Imai, and Li (2024/2025). Their methods turn the traditional 80/20 holdout and cross va...
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Survey Validation based on Synthetic Data with the Help of Claude

survey creation and validation survey creation and validation Author dr. mario martinez Published August 18, 2025 Introduction This code was generated with the help of Claude Set up synthetic data # Load necessary libraries library (survey) library (lavaan) library (psych) library (dplyr) library (ggplot2) library (missMethods) # Set seed for reproducibility set.seed ( 123 ) # Create simulated survey data n <- 500 survey_data <- data.frame ( id = 1 : n, q1 = sample ( 1 : 5 , n, replace = TRUE ), q2 = sample ( 1 : 5 , n, replace = TRUE ), q3 = sample ( 1 : 5 , n, replace = TRUE ), q4 = sample ( 1 : 5 , n, replace = TRUE ), q5 = sample ( 1 : 5 , n, replace = TRUE ), q6 = sample ( 1 : 5 , n, replace = TRUE ) ) survey_data1 <- impute_median (survey_data) # Round to whole numbers an...
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