format comparison

1st

Skill Category	Skills/Tools/Workflows
Multi-Language Workflows	- Image analysis pipeline with Python and R - Model report pipeline with R, Bash, Python, and JavaScript (Quarto) - Backup MD pipeline - MRI analysis pipeline with MATLAB, Bash, and Python
Workflow Scripts	- Organizing and comparing statistical results
Version Control	- Git for documentation, backup, collaboration, and branches for major alterations or exploration
Data Collection	- Qualtrics, Mechanical Turk - Creating tasks in JavaScript (jsPsych) for collecting human subject data
Data Quality Assessment	- Assessing respondent quality/good-faith via analysis of respondent reaction times, uniformity of response patterns, fitting contaminant models
R Programming	- Multilevel modeling - Parallel computing and remote workflows (deploying jobs across remote cluster, using supercomputer) - Quarto and Rmarkdown websites for efficient documentation and communication - Purrr for manipulating hierarchically structured data - Web scraping - Profile customization and environment handling
Tidyverse (R)	- Dplyr and Tidyr for data wrangling and summarizing - Purrr for fitting models
Shell Scripting	- Bash scripting
SQL	- Data manipulation and querying
Jupyter	- Interactive computing and data analysis
Tools	- RStudio, VS Code, Vim/Neovim - Obsidian
Computational Modeling	- Neural Networks - Exemplar Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences
Data Analysis	- Mixed Effects Models - Bayesian Statistics - Dimensionality reduction - Frequentist ANOVA/ANCOVA/Regression
R Libraries	- Data.table - Ggplot - Shiny - Brms

2nd

Category	Skills/Tools
Programming Languages	R, Python, Bash, MATLAB, JavaScript
Data Analysis	Mixed Effects Models, Bayesian Statistics, Dimensionality Reduction, Frequentist ANOVA/ANCOVA/Regression
Statistical Software	R (Tidyverse, Data.table, Ggplot, Shiny, Brms, Rmarkdown/quarto)
Workflow Automation	Multi-language workflows (R, Bash, Python, MATLAB), Workflow Scripts for statistical results, Quarto, Jupyter
Version Control	Git (documentation, branching, collaboration)
Web Technologies	Qualtrics, Mechanical Turk, JavaScript (jsPsych for human subject data collection)
Computational Modeling	Neural Networks, Exemplar Models, Similarity Scaling, Approximate Bayesian Computation, Individual Differences
Parallel Computing & Remote Workflows	Deploying jobs across remote clusters, Supercomputer usage
Software & Tools	RStudio, VS Code, Vim/Neovim, Obsidian
Data Manipulation & Visualization	Tidyverse (dplyr, tidyr, purrr), Data.table, Ggplot, Shiny
Document Creation & Reporting	Quarto, Rmarkdown, Jupyter for documenting and communicating code, results, visuals
Online Data Collection & Analysis	Assessing respondent quality, reaction times analysis, contaminant models fitting
Shell Scripting	Bash
Database Management	SQL
Professional Development	Avid consumer of R packages and best practices, regular follower of R Bloggers and R-related forums
Data Collection & Experimentation	Creating tasks for collecting human subject data (ratings, surveys, value judgements) with JavaScript
Miscellaneous	Web scraping, Profile customization and environment handling

3rd

Here’s a markdown table of your computational/technical/programming skills based on the unorganized list you provided:

Skill	Description
Multi-language workflows	- Mixing R, bash, python, MATLAB - Image analysis pipeline with python and R - Model report pipeline: R -> bash + python + javascript - quarto - Backup md pipeline - MRI analysis pipeline: MATLAB - bash - python
Workflow scripts	For organizing and comparing statistical results
Git	- Documentation and backup - Branches for major alterations or exploration - Collaboration
Qualtrics, Mechanical Turk	-
JavaScript (jspsych)	Experience creating tasks for collecting human subject data (including ratings, survey responses, value judgments)
Respondent quality assessment	Assessing respondent quality/good-faith via analysis of respondent reaction times, uniformity of response patterns, fitting contaminant models
R	- Multilevel modeling - Parallel computing and remote workflows (deploying jobs across remote cluster, using supercomputer) - Quarto and Rmarkdown websites for efficient documentation and communication of code, statistical results, and visuals - Avid consumer of the latest R packages and best practices - purrr for manipulating hierarchically structured data - Web scraping - Profile customization and environment handling
Tidyverse	Engaging with the tidyverse on a daily basis, using dplyr and tidyr for the past 4 years for wrangling and summarizing dataframes, purrr for the past year for fitting models, etc.
Data analysis	-
Shell scripting/bash	-
SQL	-
Jupyter	-
Version control	-
Tools	Rstudio, Vs Code, Vim/Neovim, Obsidian
Computational Modeling	- Neural Networks - Exemplar Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences
Data Analysis	- Mixed Effects Models - Bayesian Statistics - Dimensionality reduction - Frequentist ANOVA/ANCOVA/Regression
R	- Tidyverse: dplyr, purrr - Data.table - Ggplot - Shiny - Brms - Rmarkdown/quarto

fourth

Programming

• R
  • Rmarkdown
  • Quarto
• JavaScript
• Bash
• Python

Computational

• Artificial Neural Networks
• Exemplar Models
• Bayesian Statistics
• Mixed Effect Models

Experimental

• Behavioral Tasks
• Online Data Collection - JsPsych
• Survey Data - Qualtrics
• Mechanical Turk
• MRI

Other Skills

• Multi-language workflows
  • Mixing R, bash, python, MATLAB
  • Image analysis pipeline with python and R
  • Model report pipeline: R -> bash + python + javascript - quarto
  • Backup md pipeline
  • MRI analysis pipeline: MATLAB - bash - python
• Workflow Scripts for organizing and comparing statistical results
• Git
  • Documentation and backup
  • Branches for major alterations or exploration
  • Collaboration
• Experience creating tasks in JavaScript (jspsych) for collecting human subject data (including ratings, survey responses, value judgments)
• Assessing respondent quality/good-faith via analysis of respondent reaction times, uniformity of response patterns, fitting contaminant models
• Multilevel modeling
• Parallel computing and remote workflows
  • Deploying jobs across remote cluster
  • Using supercomputer
• Quarto and Rmarkdown websites for efficient documentation and communication of code, statistical results, and visuals
• Avid consumer of the latest R packages and best practices
  • Perusing R Bloggers and various R-related subreddits weekly
  • GitHub explore page geared around suggesting popular R repos
• purrr for manipulating hierarchically structured data
• Web scraping
• Profile customization and environment handling
• Tidyverse
  • Daily engagement with the tidyverse
  • Using dplyr and tidyr for the past 4 years for wrangling and summarizing dataframes
  • Using purrr for the past year for fitting models, etc.
• Data analysis
• Shell scripting/bash
• SQL
• Jupyter
• Version control
• Tools: Rstudio, Vs Code, Vim/Neovim, Obsidian

Fifth

Programming Languages

• R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms)
• JavaScript (Experience creating tasks for collecting human subject data with jspsych)
• Bash (Shell scripting, Workflow automation)
• Python (Data analysis, Scripting for various pipelines)

Computational Skills

• Artificial Neural Networks
• Exemplar Models
• Bayesian Statistics
• Mixed Effect Models
• Similarity Scaling
• Approximate Bayesian Computation
• Individual Differences

Data Analysis Techniques

• Multilevel Modeling
• Bayesian Statistics
• Mixed Effects Models
• Dimensionality Reduction
• Frequentist ANOVA/ANCOVA/Regression
• Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers)

Experimental Skills

• Behavioral Tasks
• Online Data Collection (jsPsych for web-based experiments)
• Survey Data Collection (Qualtrics)
• Use of Mechanical Turk for data collection
• MRI Data Analysis

Workflow Automation & Version Control

• Multi-language workflows (integrating R, Bash, Python, MATLAB)
• Git (Documentation, backup, branching for major alterations or explorations, collaboration)

Professional Development & Tools

• Avid consumer of the latest R packages and best practices, regular engagement with R Bloggers and various R-related forums.
• Tools: RStudio, VS Code, Vim/Neovim, Obsidian.

Miscellaneous Skills

• Web Scraping
• SQL for Database Management
• Jupyter for Interactive Notebooks
• Profile Customization and Environment Handling

5b

Category	Skills
Programming Languages	R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms), JavaScript (Experience creating tasks for collecting human subject data with jspsych), Bash (Shell scripting, Workflow automation), Python (Data analysis, Scripting for various pipelines)
Computational Skills	Artificial Neural Networks, Exemplar Models, Bayesian Statistics, Mixed Effect Models, Similarity Scaling, Approximate Bayesian Computation, Individual Differences
Data Analysis Techniques	Multilevel Modeling, Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction, Frequentist ANOVA/ANCOVA/Regression, Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers)
Experimental Skills	Behavioral Tasks, Online Data Collection (jsPsych for web-based experiments), Survey Data Collection (Qualtrics), Use of Mechanical Turk for data collection, MRI Data Analysis
Workflow Automation and Version Control	Multi-language workflows (integrating R, Bash, Python, MATLAB), Git (Documentation, backup, branching for major alterations or explorations, collaboration)
Professional Development and Tools	Avid consumer of the latest R packages and best practices, regular engagement with R Bloggers and various R-related forums. Tools: RStudio, VS Code, Vim/Neovim, Obsidian.
Miscellaneous Skills	Web Scraping, SQL for Database Management, Jupyter for Interactive Notebooks, Profile Customization and Environment Handling

5c

Category	Skills
Programming Languages	- R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms)
	- JavaScript (Experience creating tasks for collecting human subject data with jspsych)
	- Bash (Shell scripting, Workflow automation)
	- Python (Data analysis, Scripting for various pipelines)
Computational Skills	- Artificial Neural Networks
	- Exemplar Models
	- Bayesian Statistics
	- Mixed Effect Models
	- Similarity Scaling
	- Approximate Bayesian Computation
	- Individual Differences
Data Analysis Techniques	- Multilevel Modeling
	- Bayesian Statistics
	- Mixed Effects Models
	- Dimensionality Reduction
	- Frequentist ANOVA/ANCOVA/Regression
	- Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers)
Experimental Skills	- Behavioral Tasks
	- Online Data Collection (jsPsych for web-based experiments)
	- Survey Data Collection (Qualtrics)
	- Use of Mechanical Turk for data collection
	- MRI Data Analysis
Workflow Automation and Version Control	- Multi-language workflows (integrating R, Bash, Python, MATLAB)
	- Git (Documentation, backup, branching for major alterations or explorations, collaboration)
Professional Development and Tools	- Avid consumer of the latest R packages and best practices, regular engagement with R Bloggers and various R-related forums.
	- Tools: RStudio, VS Code, Vim/Neovim, Obsidian.
Miscellaneous Skills	- Web Scraping
	- SQL for Database Management
	- Jupyter for Interactive Notebooks
	- Profile Customization and Environment Handling

5d

Programming Languages

• R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms)
• JavaScript (Experience creating tasks for collecting human subject data with jspsych)
• Bash (Shell scripting, Workflow automation)
• Python (Data analysis, Scripting for various pipelines)

Computational Skills

• Artificial Neural Networks
• Exemplar Models
• Bayesian Statistics
• Mixed Effect Models
• Similarity Scaling
• Approximate Bayesian Computation
• Individual Differences

Data Analysis Techniques

• Multilevel Modeling
• Bayesian Statistics
• Mixed Effects Models
• Dimensionality Reduction
• Frequentist ANOVA/ANCOVA/Regression
• Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers)

Experimental Skills

• Behavioral Tasks
• Online Data Collection (jsPsych for web-based experiments)
• Survey Data Collection (Qualtrics)
• Use of Mechanical Turk for data collection
• MRI Data Analysis

Workflow Automation & Version Control

• Multi-language workflows (integrating R, Bash, Python, MATLAB)
• Git (Documentation, backup, branching for major alterations or explorations, collaboration)
• Tools: RStudio, VS Code, Vim/Neovim, Obsidian.

Miscellaneous Skills

• Web Scraping
• SQL for Database Management
• Jupyter for Interactive Notebooks
• Profile Customization and Environment Handling

5e

Programming Languages
- R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms)
- JavaScript (Experience creating tasks for collecting human subject data with jspsych)
- Bash (Shell scripting, Workflow automation)
- Python (Data analysis, Scripting for various pipelines)

Computational Skills
- Artificial Neural Networks
- Exemplar Models
- Bayesian Statistics
- Mixed Effect Models
- Similarity Scaling
- Approximate Bayesian Computation
- Individual Differences

Data Analysis Techniques
- Multilevel Modeling
- Bayesian Statistics
- Mixed Effects Models
- Dimensionality Reduction
- Frequentist ANOVA/ANCOVA/Regression
- Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers)

Experimental Skills
- Behavioral Tasks
- Online Data Collection (jsPsych for web-based experiments)
- Survey Data Collection (Qualtrics)
- Use of Mechanical Turk for data collection
- MRI Data Analysis

Workflow Automation & Version Control
- Multi-language workflows (integrating R, Bash, Python, MATLAB)
- Git (Documentation, backup, branching for major alterations or explorations, collaboration)

• Tools: RStudio, VS Code, Vim/Neovim, Obsidian.

Miscellaneous Skills
- Web Scraping
- SQL for Database Management
- Jupyter for Interactive Notebooks
- Profile Customization and Environment Handling

5f

Category	Skills
Programming Languages	R, JavaScript, Bash, Python
-	-
R Packages	Rmarkdown, Quarto, Tidyverse (dplyr, tidyr), Data.table, Ggplot, Shiny, Brms
JavaScript Libraries	jsPsych
Bash	Shell scripting, Workflow automation
Python	Data analysis, Scripting for various pipelines
Computational Skills	Artificial Neural Networks, Exemplar Models, Bayesian Statistics, Mixed Effect Models, Similarity Scaling, Approximate Bayesian Computation, Individual Differences
Data Analysis Techniques	Multilevel Modeling, Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction, Frequentist ANOVA/ANCOVA/Regression, Parallel Computing and Remote Workflows
Experimental Skills	Behavioral Tasks, Online Data Collection, Survey Data Collection, Use of Mechanical Turk for data collection, MRI Data Analysis
Workflow Automation & Version Control	Multi-language workflows, Git
Tools	RStudio, VS Code, Vim/Neovim, Obsidian, Jupyter
Miscellaneous Skills	Web Scraping, SQL for Database Management, Profile Customization and Environment Handling

In this format, the skills are organized into categories in a table format, which may make it easier to read and compare skills across categories. The first column lists the category, and the second column lists the corresponding skills. For some categories, there are subcategories listed in the first column to provide more specific information about the skills.

5g

Programming Languages:
R (Rmarkdown, Quarto, Tidyverse, Data.table, Ggplot, Shiny, Brms), JavaScript (jspsych), Bash (Shell scripting, Workflow automation), Python (Data analysis, Scripting)

Computational Skills:
Artificial Neural Networks, Exemplar Models, Bayesian Statistics, Mixed Effect Models, Similarity Scaling, Approximate Bayesian Computation, Individual Differences

Data Analysis Techniques:
Multilevel Modeling, Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction, Frequentist ANOVA/ANCOVA/Regression, Parallel Computing and Remote Workflows

Experimental Skills:
Behavioral Tasks, Online Data Collection (jsPsych), Survey Data Collection (Qualtrics), Use of Mechanical Turk, MRI Data Analysis

Workflow Automation & Version Control:
Multi-language workflows (R, Bash, Python, MATLAB), Git (Documentation, backup, branching, collaboration)

Tools:
RStudio, VS Code, Vim/Neovim, Obsidian

Miscellaneous Skills:
Web Scraping, SQL for Database Management, Jupyter for Interactive Notebooks, Profile Customization and Environment Handling

5h

Category	Skills
Programming Languages	R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms), JavaScript (Experience creating tasks for collecting human subject data with jspsych), Bash (Shell scripting, Workflow automation), Python (Data analysis, Scripting for various pipelines)
Computational Skills	Artificial Neural Networks, Exemplar Models, Bayesian Statistics, Mixed Effect Models, Similarity Scaling, Approximate Bayesian Computation, Individual Differences
Data Analysis Techniques	Multilevel Modeling, Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction, Frequentist ANOVA/ANCOVA/Regression, Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers)
Experimental Skills	Behavioral Tasks, Online Data Collection (jsPsych for web-based experiments), Survey Data Collection (Qualtrics), Use of Mechanical Turk for data collection, MRI Data Analysis
Workflow Automation & Version Control	Multi-language workflows (integrating R, Bash, Python, MATLAB), Git (Documentation, backup, branching for major alterations or explorations, collaboration)
Tools	RStudio, VS Code, Vim/Neovim, Obsidian, Jupyter
Miscellaneous Skills	Web Scraping, SQL for Database Management, Profile Customization and Environment Handling

5i

Programming Languages: R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms), JavaScript (Experience creating tasks for collecting human subject data with jspsych), Bash (Shell scripting, Workflow automation), Python (Data analysis, Scripting for various pipelines).

Computational Skills: Artificial Neural Networks, Exemplar Models, Bayesian Statistics, Mixed Effect Models, Similarity Scaling, Approximate Bayesian Computation, Individual Differences.

Data Analysis Techniques: Multilevel Modeling, Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction, Frequentist ANOVA/ANCOVA/Regression, Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers).

Experimental Skills: Behavioral Tasks, Online Data Collection (jsPsych for web-based experiments), Survey Data Collection (Qualtrics), Use of Mechanical Turk for data collection, MRI Data Analysis.

Workflow Automation & Version Control: Multi-language workflows (integrating R, Bash, Python, MATLAB), Git (Documentation, backup, branching for major alterations or explorations, collaboration).

Tools: RStudio, VS Code, Vim/Neovim, Obsidian.

Miscellaneous Skills: Web Scraping, SQL for Database Management, Jupyter for Interactive Notebooks, Profile Customization and Environment Handling.

5j

Category	Skills
Programming Languages	- R: Rmarkdown, Quarto, Tidyverse (dplyr, tidyr), Data.table, Ggplot, Shiny, Brms - JavaScript: Experience with jspsych for human subject data collection - Bash: Shell scripting, Workflow automation - Python: Data analysis, Scripting
Computational Skills	- Artificial Neural Networks - Exemplar Models - Bayesian Statistics - Mixed Effect Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences
Data Analysis Techniques	- Multilevel Modeling - Bayesian Statistics - Mixed Effects Models - Dimensionality Reduction - Frequentist ANOVA/ANCOVA/Regression - Parallel Computing and Remote Workflows: Deploying jobs across remote clusters, Using supercomputers
Experimental Skills	- Behavioral Tasks - Online Data Collection: jsPsych - Survey Data Collection: Qualtrics - Use of Mechanical Turk for data collection - MRI Data Analysis
Workflow Automation & Version Control	- Multi-language workflows: Integrating R, Bash, Python, MATLAB - Git: Documentation, backup, branching for major alterations or explorations, collaboration
Tools	- RStudio - VS Code - Vim/Neovim - Obsidian - Jupyter
Miscellaneous Skills	- Web Scraping - SQL for Database Management - Profile Customization and Environment Handling

5k

Category	Skills
Programming Languages	- R (Rmarkdown, Quarto, Tidyverse including dplyr and tidyr, Data.table, Ggplot, Shiny, Brms) - JavaScript (Experience creating tasks for collecting human subject data with jspsych) - Bash (Shell scripting, Workflow automation) - Python (Data analysis, Scripting for various pipelines)
Computational Skills	- Artificial Neural Networks - Exemplar Models - Bayesian Statistics - Mixed Effect Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences
Data Analysis Techniques	- Multilevel Modeling - Bayesian Statistics - Mixed Effects Models - Dimensionality Reduction - Frequentist ANOVA/ANCOVA/Regression - Parallel Computing and Remote Workflows (Deploying jobs across remote clusters, Using supercomputers)
Experimental Skills	- Behavioral Tasks - Online Data Collection (jsPsych for web-based experiments) - Survey Data Collection (Qualtrics) - Use of Mechanical Turk for data collection - MRI Data Analysis
Workflow Automation & Version Control	- Multi-language workflows (integrating R, Bash, Python, MATLAB) - Git (Documentation, backup, branching for major alterations or explorations, collaboration)
Tools	- RStudio - VS Code - Vim/Neovim - Obsidian - Jupyter
Miscellaneous Skills	- Web Scraping - SQL for Database Management - Profile Customization and Environment Handling

5l

Category	Skills	Details
Programming Languages	- R - JavaScript - Bash - Python	R: Rmarkdown, Quarto, Tidyverse (dplyr, tidyr), Data.table, Ggplot, Shiny, Brms JavaScript: Experience with jspsych for human subject data collection Bash: Shell scripting, Workflow automation Python: Data analysis, Scripting
Computational Skills	- Artificial Neural Networks - Exemplar Models - Bayesian Statistics - Mixed Effect Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences	Utilizing complex models and statistical approaches for data analysis and predictive modeling
Data Analysis Techniques	- Multilevel Modeling - Bayesian Statistics - Mixed Effects Models - Dimensionality Reduction - Frequentist Statistics - Parallel Computing	Advanced statistical methods for data analysis, including use of remote workflows and computing resources
Experimental Skills	- Behavioral Tasks - Online Data Collection - Survey Data Collection - Use of Mechanical Turk - MRI Data Analysis	Designing and conducting experiments, collecting and analyzing data from various sources
Workflow Automation & Version Control	- Multi-language workflows - Git	Integrating R, Bash, Python, MATLAB for streamlined processes; using Git for version control
Tools	- RStudio - VS Code - Vim/Neovim - Obsidian - Jupyter	Essential software tools for development, analysis, and documentation
Miscellaneous Skills	- Web Scraping - SQL - Profile Customization - Environment Handling	Techniques for data extraction, database management, and optimizing computing environments

5m

Skills

Category	Skills
Programming	R (Quarto, Rmarkdown, Shiny); JavaScript; Python (jupyter, torch, tensorflow); Bash (Shell scripting, Workflow automation); MATLAB (psychtoolbox)
Computational Modeling	Artificial Neural Networks; Exemplar Models; Bayesian Statistics; Mixed Effect Models; Similarity Scaling; Approximate Bayesian Computation; Individual Differences
Data Analysis	Multilevel Modeling; Bayesian Statistics; Mixed Effects Models; Dimensionality Reduction; Frequentist ANOVA/Regression
Experimental Skills	Behavioral Tasks; Online Data Collection (jsPsych for web-based experiments); Survey Data Collection (Qualtrics); Mechanical Turk; MRI & EEG
Workflow Automation & Version Control	Multi-language workflows (integrating R, Bash, Python); Git (Documentation, backup, branching, collaboration)
Tools	RStudio; VS Code; Vim/Neovim; Obsidian
Miscellaneous Skills	Web Scraping; SQL; Website Design

5n

Skills

Category	Skills
Programming	- R (Quarto, Rmarkdown, Shiny) - JavaScript - Python (jupyter, torch, tensorflow) - Bash (Shell scripting, Workflow automation) - MATLAB (psychtoolbox)
Computational Modeling	- Artificial Neural Networks - Exemplar Models - Bayesian Statistics - Mixed Effect Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences
Data Analysis	- Multilevel Modeling - Bayesian Statistics - Mixed Effects Models - Dimensionality Reduction - Frequentist ANOVA/Regression
Experimental Skills	- Behavioral Tasks - Online Data Collection (jsPsych for web-based experiments) - Survey Data Collection (Qualtrics) - Mechanical Turk - MRI & EEG
Workflow Automation & Version Control	- Multi-language workflows (integrating R, Bash, Python) - Git (Documentation, backup, branching, collaboration)
Tools	- RStudio - VS Code - Vim/Neovim - Obsidian
Miscellaneous Skills	- Web Scraping - SQL - Website Design

5m Skills

Category	Skills
Programming	- R (Quarto, Rmarkdown, Shiny) - JavaScript - Python (jupyter, torch, tensorflow) - Bash (Shell scripting, Workflow automation) - MATLAB (psychtoolbox)
Computational Modeling	- Artificial Neural Networks - Exemplar Models - Bayesian Statistics - Mixed Effect Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences
Data Analysis	- Multilevel Modeling - Bayesian Statistics - Mixed Effects Models - Dimensionality Reduction - Frequentist ANOVA/Regression
Experimental Skills	- Behavioral Tasks - Online Data Collection (jsPsych for web-based experiments) - Survey Data Collection (Qualtrics) - Mechanical Turk - MRI & EEG
Workflow Automation & Version Control	- Multi-language workflows (integrating R, Bash, Python) - Git (Documentation, backup, branching, collaboration)
Tools	- RStudio - VS Code - Vim/Neovim - Obsidian
Miscellaneous Skills	- Web Scraping - SQL - Website Design

Skills

Category	Skills
Programming	- R (Quarto, Rmarkdown, Shiny) - JavaScript - Python (jupyter, torch, tensorflow) - Bash (Shell scripting, Workflow automation) - MATLAB (psychtoolbox)
Computational Modeling	- Artificial Neural Networks - Exemplar Models - Bayesian Statistics - Mixed Effect Models - Similarity Scaling - Approximate Bayesian Computation - Individual Differences
Data Analysis	- Multilevel Modeling - Bayesian Statistics - Mixed Effects Models - Dimensionality Reduction - Frequentist ANOVA/Regression
Experimental Skills	- Behavioral Tasks - Online Data Collection (jsPsych for web-based experiments) - Survey Data Collection (Qualtrics) - Mechanical Turk - MRI & EEG
Workflow Automation & Version Control	- Multi-language workflows (integrating R, Bash, Python) - Git (Documentation, backup, branching, collaboration)
Tools	- RStudio - VS Code - Vim/Neovim - Obsidian
Miscellaneous Skills	- Web Scraping - SQL - Website Design

To ensure that the PDF version of your Quarto document renders the skills column with separate lines for each dashed item, you can use HTML tags within the Markdown table. Here’s the updated table:

Skills

Category	Skills
Programming
Computational Modeling
Data Analysis
Experimental Skills
Workflow Automation & Version Control
Tools
Miscellaneous Skills

In this updated version, I replaced the dashed items with <ul> (unordered list) and <li> (list item) HTML tags. Each skill is now wrapped inside <li> tags, and the entire list is enclosed within <ul> tags.

By using HTML tags, the PDF version of your Quarto document will render the skills column with separate lines for each item, just like in the HTML version.

Skills

Category	Skills
Programming	- R (Quarto, Rmarkdown, Shiny )
	- JavaScript
	- Python (jupyter, torch, tensorflow)
	- Bash (Shell scripting, Workflow automation)
	- MATLAB (psychtoolbox)
Computational Modeling	- Artificial Neural Networks
	- Exemplar Models
	- Bayesian Statistics
	- Mixed Effect Models
	- Similarity Scaling
	- Approximate Bayesian Computation
	- Individual Differences
Data Analysis	- Multilevel Modeling
	- Bayesian Statistics
	- Mixed Effects Models
	- Dimensionality Reduction
	- Frequentist ANOVA/Regression
Experimental Skills	- Behavioral Tasks
	- Online Data Collection (jsPsych for web-based experiments)
	- Survey Data Collection (Qualtrics)
	- Mechanical Turk
	- MRI & EEG
Workflow Automation & Version Control	- Multi-language workflows (integrating R, Bash, Python)
	- Git (Documentation, backup, branching, collaboration)
Tools	- RStudio
	- VS Code
	- Vim/Neovim
	- Obsidian
Miscellaneous Skills	- Web Scraping
	- SQL
	- Website Design

Sixth

Computational and Programming Skills

• Programming Languages: Proficient in R (Rmarkdown, Quarto), JavaScript, Bash, and Python. Experience with multi-language workflows, including mixing R, Bash, Python, and MATLAB.

  • R: Skilled in multilevel modeling, parallel computing, and remote workflows. Experience with Quarto and Rmarkdown for efficient documentation and communication of code, statistical results, and visuals. Avid consumer of the latest R packages and best practices. Proficient in using purrr to manipulate hierarchically structured data and web scraping.

  • Tidyverse: Daily engagement with the tidyverse, using dplyr and tidyr for the past 4 years for data wrangling and summarizing. Proficient in using purrr for fitting models.

  • Python: Experience with image analysis pipeline and MRI analysis pipeline using Python.

  • Bash: Experience with shell scripting and workflow scripts for organizing and comparing statistical results.

  • JavaScript: Experience creating tasks for collecting human subject data using jsPsych.

• Version Control: Proficient in using Git for documentation, backup, collaboration, and branching for major alterations or exploration.

• Tools: Experience with RStudio, VS Code, Vim/Neovim, Obsidian, Jupyter, and SQL.

Data Analysis Skills

• Statistical Analysis: Proficient in Mixed Effects Models, Bayesian Statistics, Frequentist ANOVA/ANCOVA/Regression, and Dimensionality Reduction. Experience with brms package in R for Bayesian modeling.

• Data Wrangling and Visualization: Proficient in using data.table, ggplot, and Shiny in R for data manipulation, visualization, and creating interactive web applications.

• Data Collection and Quality Assessment: Experience with online data collection using Qualtrics and Mechanical Turk. Skilled in assessing respondent quality/good-faith via analysis of respondent reaction times, uniformity of response patterns, and fitting contaminant models.

Computational Modelling Skills

• Neural Networks: Experience with Artificial Neural Networks for computational modeling.

• Exemplar Models: Experience with Exemplar Models for computational modeling.

• Similarity Scaling: Experience with Similarity Scaling for computational modeling.

• Approximate Bayesian Computation: Experience with Approximate Bayesian Computation for computational modeling.

• Individual Differences: Experience with modeling Individual Differences in computational modeling.

Experimental Skills

• Behavioral Tasks: Experience with creating and conducting behavioral tasks.

• Online Data Collection: Experience with online data collection using tools like JsPsych.

• Survey Data: Experience with collecting and analyzing survey data using Qualtrics.

• MRI: Experience with MRI data analysis pipeline using MATLAB, Bash, and Python.

Seventh - Condensed

Programming Languages and Tools

• R: Advanced data analysis, visualization (Ggplot, Shiny), modeling (Brms), and automation (Rmarkdown, Quarto).
• Python: Data processing, modeling, and analysis in cognitive science research.
• MATLAB: Experience with MRI data analysis and modeling workflows.
• Bash: Scripting for data processing and automation of workflows.
• JavaScript: Developing behavioral and cognitive tasks with jsPsych for online data collection.

Computational Modeling and Data Analysis

• Artificial Neural Networks & Exemplar Models: Expertise in simulating cognitive processes and pattern recognition.
• Bayesian Statistics & Mixed Effect Models: Advanced statistical analysis for cognitive science research.
• Approximate Bayesian Computation & Dimensionality Reduction: Techniques for complex model fitting and data simplification.
• Multilevel Modeling: Handling nested or hierarchical data structures common in cognitive and behavioral data.

Experimental Skills

• Online Data Collection: Designing and implementing studies using Qualtrics and jsPsych.
• MRI Data Analysis: Processing and analyzing neuroimaging data to understand cognitive functions.

Version Control and Collaboration

• Git: Proficient in using Git for version control, collaboration, and documentation of research projects.

Professional Engagement

• Active participant in the computational cognitive science community, staying updated with the latest methods and practices through forums, blogs, and GitHub.

Eigth

Computational and Programming Skills

• Programming Languages: Proficient in R, Python, and Bash. Experience with multi-language workflows, including mixing R, Bash, and Python.

  • R: Skilled in multilevel modeling, parallel computing, and remote workflows. Experience with Rmarkdown and Quarto for efficient documentation and communication of code, statistical results, and visuals. Proficient in using purrr to manipulate hierarchically structured data.

  • Python: Experience with image analysis pipeline and MRI analysis pipeline using Python.

  • Bash: Experience with shell scripting and workflow scripts for organizing and comparing statistical results.

• Version Control: Proficient in using Git for documentation, backup, collaboration, and branching for major alterations or exploration.

• Tools: Experience with RStudio, VS Code, Jupyter, and SQL.

Data Analysis Skills

• Statistical Analysis: Proficient in Mixed Effects Models, Bayesian Statistics, and Dimensionality Reduction. Experience with brms package in R for Bayesian modeling.

• Data Wrangling and Visualization: Proficient in using data.table and ggplot in R for data manipulation and visualization.

Computational Modelling Skills

• Neural Networks: Experience with Artificial Neural Networks for computational modeling.

• Exemplar Models: Experience with Exemplar Models for computational modeling.

• Approximate Bayesian Computation: Experience with Approximate Bayesian Computation for computational modeling.

• Individual Differences: Experience with modeling Individual Differences in computational modeling.

Experimental Skills

• Behavioral Tasks: Experience with creating and conducting behavioral tasks.

• Online Data Collection: Experience with online data collection using tools like JsPsych.

• MRI: Experience with MRI data analysis pipeline using MATLAB, Bash, and Python.Geplaatst Sure, here’s a more condensed version of your computational and programming skills, tailored for a post-doctoral computational cognitive modeling CV:

Computational and Programming Skills

• Languages: Proficient in R, Python, and Bash. Experience with multi-language workflows.
  • R: Skilled in multilevel modeling, parallel computing, and remote workflows. Experience with Rmarkdown and Quarto for documentation. Proficient in using purrr for data manipulation.
  • Python: Experience with image and MRI analysis pipelines.
  • Bash: Experience with shell scripting and workflow organization.
• Version Control: Proficient in Git for documentation, backup, and collaboration.
• Tools: Experience with RStudio, VS Code, Jupyter, and SQL.

Data Analysis Skills

• Statistical Analysis: Proficient in Mixed Effects Models, Bayesian Statistics, and Dimensionality Reduction. Experience with brms package in R for Bayesian modeling.
• Data Wrangling and Visualization: Proficient in using data.table and ggplot in R.

Computational Modeling Skills

• Neural Networks: Experience with Artificial Neural Networks.
• Exemplar Models: Experience with Exemplar Models.
• Approximate Bayesian Computation: Experience with Approximate Bayesian Computation.
• Individual Differences: Experience with modeling Individual Differences.

Experimental Skills

• Behavioral Tasks: Experience with creating and conducting behavioral tasks.
• Online Data Collection: Experience with online data collection using tools like JsPsych.
• MRI: Experience with MRI data analysis pipeline using MATLAB, Bash, and Python.

Ninth

Programming

• R (Tidyverse, dplyr, purrr, data.table, ggplot, shiny, brms, Rmarkdown, Quarto)
• JavaScript (jspsych for online experiments)
• Bash
• Python

Computational Modeling

• Artificial Neural Networks
• Exemplar Models
• Bayesian Statistics
• Mixed Effect Models
• Approximate Bayesian Computation
• Individual Differences Modeling

Data Analysis

• Multilevel/Hierarchical Modeling
• Bayesian Analysis
• Dimensionality Reduction
• ANOVA/ANCOVA/Regression

Experimental

• Behavioral Tasks
• Online Data Collection (JsPsych, Mechanical Turk)
• Survey Data (Qualtrics)
• MRI Analysis Pipeline (MATLAB, bash, python)

Workflows and Tools

• Multi-language Workflows (R, bash, python, MATLAB)
• Parallel Computing and Remote Workflows (Cluster, Supercomputer)
• Git (Version Control, Collaboration, Branching)
• Quarto and Rmarkdown (Documentation and Communication)
• Rstudio, Vs Code, Vim/Neovim, Obsidian
• Web Scraping
• Environment Handling and Profile Customization

Tenth

Programming

• R: Proficient in Rmarkdown and Quarto; strong understanding of Tidyverse principles (dplyr, purrr, tidyr) and data.table
• Python: Experience with image analysis and report generation pipelines
• JavaScript: Used to design jsPsych tasks for human subject data collection
• Bash: Utilized for workflow scripts and MRI analysis pipelines

Data Analysis

• Statistical Techniques: Mixed effect models, Bayesian statistics, frequentist approaches (ANOVA/ANCOVA/regression), dimensionality reduction
• R Packages: brms, ggplot2, Shiny
• Data Cleaning & QA: Contaminant model fitting, reaction time/response pattern analysis for respondent quality assessment

Computational Modeling

• Artificial neural networks
• Exemplar models
• Similarity scaling
• Approximate Bayesian Computation
• Individual differences modeling

Workflow Expertise

• Multi-language Integration: Successfully combine R, bash, python, and MATLAB in analysis pipelines
• Git: Utilizing Git for version control, documentation, backup, and collaboration
• Remote Workflows: Experience deploying jobs to remote clusters and on supercomputers
• Documentation: Employing Quarto and Rmarkdown for reproducible, web-based reporting

Additional Skills

• Qualtrics & Mechanical Turk: Task creation, data handling
• Web Scraping
• Shell Scripting (bash)
• SQL
• Jupyter
• Environment Customization Proficient in RStudio, VS Code, Vim/Neovim, and Obsidian

11th

Skills

• Programming: R (Tidyverse, data.table), Python, JavaScript, Bash
• Computational Modeling: Neural networks, exemplar models, similarity scaling, Approximate Bayesian Computation, individual differences modeling
• Statistical Analysis: Mixed-effects models, Bayesian statistics
• Data Analysis (R): brms, ggplot2
• Workflows: Multi-language integration (R, Python, bash, MATLAB), Git (version control, collaboration, documentation)
• Data Collection (Online): Experience with jsPsych, Qualtrics, Mechanical Turk, including respondent quality assessment

Additional Relevant Skills

• Data cleaning and wrangling
• Workflow optimization and scripting
• Experience with remote computing resources
• Strong communication and documentation skills using Quarto/Rmarkdown

12th

Area	Skills & Tools
Programming Languages	R (Ggplot, Shiny, Brms, Rmarkdown, Quarto), Python, MATLAB, Bash, JavaScript (jsPsych)
Computational Modeling	Artificial Neural Networks, Exemplar Models, Bayesian Statistics, Mixed Effect Models, Approximate Bayesian Computation, Dimensionality Reduction
Data Analysis	Multilevel Modeling, Bayesian Statistics, Dimensionality Reduction
Experimental Skills	Online Data Collection (Qualtrics, jsPsych), MRI Data Analysis
Version Control	Git (Version control, Collaboration, Documentation)
Professional Engagement	Active in computational cognitive science community forums, blogs, and GitHub

13th

Category	Skills
Programming Languages	R, Python, Bash
Multilevel Modeling	R
Parallel Computing	R
Remote Workflows	R
Documentation	Rmarkdown, Quarto
Data Manipulation	purrr
Image Analysis	Python
MRI Analysis	Python
Shell Scripting	Bash
Workflow Organization	Bash
Version Control	Git
Tools	RStudio, VS Code, Jupyter, SQL
Statistical Analysis	Mixed Effects Models, Bayesian Statistics, Dimensionality Reduction
Bayesian Modeling	brms (R package)
Data Wrangling	data.table (R package)
Data Visualization	ggplot (R package)
Neural Networks	Artificial Neural Networks
Exemplar Models
Approximate Bayesian Computation
Individual Differences
Behavioral Tasks	Creation and conduction
Online Data Collection	JsPsych
MRI Data Analysis	MATLAB, Bash, Python

14th

Programming	Computational Modeling	Data Analysis	Experimental	Workflows and Tools
- R (Tidyverse, dplyr, purrr, data.table, ggplot, shiny, brms, Rmarkdown, Quarto)	- Artificial Neural Networks	- Multilevel/Hierarchical Modeling	- Behavioral Tasks	- Multi-language Workflows (R, bash, python, MATLAB)
- JavaScript (jspsych for online experiments)	- Exemplar Models	- Bayesian Analysis	- Online Data Collection (JsPsych, Mechanical Turk)	- Parallel Computing and Remote Workflows (Cluster, Supercomputer)
- Bash	- Bayesian Statistics	- Dimensionality Reduction	- Survey Data (Qualtrics)	- Git (Version Control, Collaboration, Branching)
- Python	- Mixed Effect Models	- ANOVA/ANCOVA/Regression	- MRI Analysis Pipeline (MATLAB, bash, python)	- Quarto and Rmarkdown (Documentation and Communication)
	- Approximate Bayesian Computation			- Rstudio, Vs Code, Vim/Neovim, Obsidian
	- Individual Differences Modeling			- Web Scraping
				- Environment Handling and Profile Customization

15th

Category	Skills
Programming	R (Tidyverse, data.table), Python, JavaScript, Bash
Computational Modeling	Neural networks, exemplar models, similarity scaling, Approximate Bayesian Computation, individual differences modeling
Statistical Analysis	Mixed-effects models, Bayesian statistics (brms)
Data Visualization	ggplot2
Workflows	Multi-language integration (R, Python, bash, MATLAB), Git (version control, collaboration, documentation), Remote computing
Online Data Collection	jsPsych, Qualtrics, Mechanical Turk (including respondent quality assessment)

16th

Skills Table

Skill Area	Tools & Techniques
Data Manipulation	dplyr, tidyr, data.table, purrr
Data Visualization	ggplot2, patchwork, ggstance
Statistical Modeling	Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction
Computational Modeling	Neural Networks, Exemplar Models, Similarity Scaling, Approximate Bayesian Computation
Simulation & Analysis	Custom simulation functions, Model fitting and evaluation with ABC
Workflow Automation	Rmarkdown, Quarto, here, future, furrr
Version Control & Collaboration	Git
Experimentation Tools	Online Data Collection (Qualtrics, Mechanical Turk), JavaScript (jsPsych)
Advanced R Usage	Advanced function development, Use of parallel computing for efficient data processing
Statistical Packages	Use of specialized statistical packages like brms for Bayesian modeling

17th

Write-Up:

In my recent projects, I have demonstrated a comprehensive ability to utilize R for advanced computational modeling, data analysis, and simulation tasks. My work encompasses developing and implementing complex models, such as Artificial Learning Models (ALM) and Examination Models (Exam Models), to simulate cognitive processes and predict experimental outcomes. I am proficient in using a wide array of R packages (dplyr, purrr, tidyr, ggplot2, data.table, future, furrr, abc, and others) for data manipulation, visualization, parallel computing, and statistical analysis.

I have developed sophisticated simulation functions to analyze and predict behaviors in cognitive experiments. These functions incorporate advanced statistical techniques, including Bayesian Statistics, Approximate Bayesian Computation, and Mixed Effects Models, to interpret the nuanced dynamics of cognitive phenomena. My expertise also includes designing and executing simulations that account for individual differences and learning patterns across varied conditions and tasks.

I am adept at leveraging R’s capabilities for efficient data processing, from preprocessing and cleaning to complex transformations and analyses. My approach to data analysis is both methodical and innovative, ensuring that insights drawn are both robust and insightful. I have extensively used RMarkdown and Quarto for reproducible research, enabling clear documentation and dissemination of findings. My work demonstrates a commitment to advancing the field of cognitive modeling through rigorous analysis, creative problem-solving, and the application of cutting-edge computational techniques.

Table Format:

Skill Area	Tools & Techniques
Programming & Scripting	R, RMarkdown, Quarto, Bash, JavaScript
Computational Modeling	Artificial Neural Networks, Exemplar Models, Bayesian Statistics, Mixed Effect Models, Approximate Bayesian Computation
Data Manipulation & Analysis	dplyr, purrr, tidyr, ggplot2, data.table
Statistical Analysis	Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction, Frequentist ANOVA/ANCOVA/Regression
Visualization	ggplot2, patchwork, Visualization of Computational Models and Statistical Results
Parallel & Distributed Computing	future, furrr for efficient handling of computational tasks in parallel
Reproducible Research	RMarkdown, Quarto for documentation and presentation of research findings
Software & Tools	RStudio, VS Code, Git for version control and collaboration

18th

Skills Table

Skill Area	Proficiencies
Data Manipulation	Proficient with dplyr, purrr, tidyr, data.table for complex data wrangling tasks.
Data Visualization	Advanced use of ggplot2 for creating informative and complex plots.
Statistical Analysis	Experience with Bayesian statistics, mixed effects models, dimensionality reduction, frequentist ANOVA/ANCOVA/Regression.
Computational Modeling	Implementation of Approximate Bayesian Computation (ABC), artificial neural networks, exemplar models, and more.
Simulation Functions	Custom simulation functions for cognitive models, demonstrating a deep understanding of the modeling process.
Parallel Computing	Utilizing future and furrr for parallel processing to optimize computational efficiency.
Version Control	Using Git for source code management, indicating good practices in software development and collaboration.
Package Management	Using pacman for package management to streamline workflow and ensure reproducibility.
Scripting & Automation	Writing complex scripts for automating data analysis, model fitting, and reporting results, showcasing a high level of automation in R.

19th

Writeup

I am proficient in developing complex data analysis pipelines and computational models using R, focusing on cognitive science research. My expertise encompasses the entire data science workflow, from preprocessing and analysis to modeling and visualization. I have designed and implemented advanced computational models, including artificial neural networks and exemplar models, to investigate cognitive processes. My work often involves multi-language workflows, integrating R with Bash, Python, MATLAB, and JavaScript, to create robust analysis pipelines.

In my recent projects, I’ve utilized a variety of R packages and methodologies to perform sophisticated statistical analyses and modeling, including Bayesian statistics, mixed effect models, and dimensionality reduction. My proficiency in Rmarkdown and Quarto enables me to efficiently document and communicate code, statistical results, and visuals. I actively contribute to collaborative projects using Git for version control and collaboration, ensuring reproducibility and efficient team workflow.

Table of Programming and Modelling/Statistics Skills with R

| Skill Area                | Tools & Methods                                                                                              |
|---------------------------|--------------------------------------------------------------------------------------------------------------|
| **Data Manipulation**     | dplyr, tidyr, data.table                                                                                     |
| **Data Visualization**    | ggplot2, patchwork                                                                                           |
| **Statistical Analysis**  | Bayesian Statistics, Mixed Effects Models, Dimensionality Reduction, Frequentist Statistics                  |
| **Computational Modeling**| Neural Networks, Exemplar Models, Similarity Scaling, Approximate Bayesian Computation                      |
| **Simulation**            | Custom simulation functions for modeling and data analysis                                                   |
| **Documenting & Reporting**| Rmarkdown, Quarto, knitr, flextable, htmltools, kableExtra                                                   |
| **Version Control & Collaboration**| Git                                                                                                           |
| **Workflow Automation**   | purrr, future, furrr for parallel computing                                                                  |
| **Web Scraping**          | rvest (not explicitly mentioned but inferred as a common task requiring R skills)                            |
| **Environment & Project Management**| here, conflicted (for namespace management)                                                                  |
| **Interactive Web Applications**| Shiny (for building web apps based on R)                                                                       |

20th

• Utilize various R packages: You effectively use packages like dplyr, purrr, tidyr, ggplot2, data.table, and others for data manipulation, analysis, and visualization.
• Implement advanced statistical techniques: You implement Approximate Bayesian Computation (ABC) for parameter estimation, showcasing your understanding of Bayesian methods and complex algorithms.
• Develop custom functions: You create various functions like samp_priors, reject_abc, and run_abc_tests to modularize your code and perform specific tasks, demonstrating good programming practices.
• Work with complex data structures: You handle data frames, lists, and matrices efficiently, showcasing your ability to manage different data formats.
• Perform data cleaning and manipulation: You use functions like filter, mutate, and group_by to clean and prepare data for analysis.
• Generate informative visualizations: You create various plots like bar charts, line graphs, and residual plots to visualize data and model results.
• Utilize parallel processing: You leverage packages like future and furrr to improve computational efficiency by parallelizing tasks.

Table of R Skills:

Skill Category	Specific Skills	Evidence from Code
Programming
Data manipulation	dplyr functions (filter, mutate, group_by, etc.), data.table operations	Data cleaning and preparation throughout the script
Custom functions	Defining and using functions with arguments and return values	samp_priors, reject_abc, run_abc_tests, etc.
Loops and conditionals	for loops, while loops, ifelse statements	Iteration and logic control in reject_abc
Packages	Loading and utilizing various R packages	pacman::p_load, specific package functions used throughout
Parallel processing	Using future and furrr for parallel execution	run_abc_tests function
Modelling/Statistics
Bayesian methods	Implementing Approximate Bayesian Computation (ABC)	reject_abc function
Mixed models	Fitting Bayesian mixed effects regression models	Code not shown, but mentioned in the write-up
Statistical summaries	Calculating means, medians, standard deviations	test_summary_table function
Data visualization	Creating various plots with ggplot2	Bar charts, line graphs, residual plots

21st

Table:

Skill	Proficiency Level	Description
R Programming	Advanced	Demonstrated ability to write clean, well-structured code using advanced R programming concepts such as functions, control structures, data manipulation, and visualization.
Model Implementation	Advanced	Implemented the Adaptive Linear Model (ALM) and its extension, the Exam Generalization, from scratch using R. Defined core functions such as input activation, output activation, output probability, mean response, and weight update mechanism based on feedback.
Simulation Studies	Advanced	Developed and executed simulation functions to evaluate the performance of the ALM and Exam Generalization under various conditions. Utilized functions such as alm.sim, simOrganize, generateSimData, and simulateAll to generate data, simulate the training process, and test the models’ generalization abilities.
Data Analysis & Visualization	Intermediate	Experience in analyzing and visualizing data using R packages such as dplyr and ggplot2. Able to create meaningful visualizations to represent complex data and relationships.
Object-Oriented Programming	Intermediate	Familiarity with object-oriented programming concepts in R, such as creating and manipulating objects, understanding inheritance, and utilizing methods associated with objects.

22nd

I am highly proficient in using R for data analysis, modelling and visualization. The code sample demonstrates my strong capabilities across several key areas:

• Data manipulation and transformation using packages like dplyr, data.table, tidyr and purrr. I can efficiently wrangle and reshape datasets into suitable formats for analysis.

• Parallel computing with the future and furrr packages to speed up compute-intensive tasks. I know how to leverage multi-core architectures to dramatically improve runtime.

• Implementation of advanced modelling techniques like Approximate Bayesian Computation (ABC) for simulation-based parameter inference. I constructed flexible functions to run the ABC algorithm under various configurations.

• Statistical modelling of experimental psychology data, including hierarchical models with subject-level parameters. I can fit and compare different cognitive models to behavioral data.

• Production of publication-quality figures using ggplot2. I created multi-panel plots to visualize model fits and compare residuals across experimental conditions.

• Dynamic report generation with R Markdown, knitr and related packages. The code snippets include cache settings and figure options, demonstrating knowledge of these tools.

In summary, I have extensive experience applying R to real-world data analysis problems, especially in the domain of cognitive modelling. My skills span the entire pipeline from data ingestion and cleaning to statistical inference and communication of results.

Skill	Level	Description
Data Manipulation	Expert	Fluent in dplyr, data.table, purrr, and tidyr
Parallel Computing	Advanced	Experience with future and furrr for parallel execution
Statistical Modelling	Expert	Implemented ABC for simulation-based inference, applied to hierarchical cognitive models
Machine Learning	Intermediate	Some exposure to ML techniques like Bayesian optimization
Data Visualization	Expert	Created publication-quality multi-panel figures with ggplot2
Reproducibility	Advanced	Used R Markdown and knitr to generate dynamic reports, set caching options
Domain Expertise	Advanced	Deep knowledge of cognitive modelling and experimental design in psychology

tegorman@iu.edu	tegorman13@gmail.com
Google Scholar	ORCiD
GitHub	ResearchGate

Icon	Contact Information
	tegorman@iu.edu
	tegorman13@gmail.com
	Google Scholar
	ORCiD
	GitHub
	ResearchGate

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	tegorman@iu.edu
	tegorman13@gmail.com
	Google Scholar
	ORCiD
	GitHub
	ResearchGate

Contact	Profiles	Research
tegorman@iu.edu	Google Scholar	GitHub
tegorman13@gmail.com	ORCiD	ResearchGate