lnalborczyk Goto Github PK

automated_sequential_analyses

Materials for the paper "A fully automated, transparent, reproducible, and blind protocol for sequential analyses" (Beffara, Bret, & Nalborczyk, 2021).

bayes_ugent_2018

ddm_emo_stroop

Joint modelling of accuracy and reaction times using diffusion models

doodle_reminder

ester

Efficient Sequential Testing with (Information Criteria and) Evidence Ratios

ibsm2023

Workshop - Introduction to Bayesian statistical modelling

ilcb_deep_learning_2022

Introduction to deep learning in R and Python.

imsb2022

Formation doctorale d'introduction à la modélisation statistique bayésienne

inner_experience_emg

Re-analysing the data from Moffatt et al. (2020): What can we learn from an under-powered absence of difference?

intro_bayes_jamovi_master

Slides available at https://tinyurl.com/introbayes2022.

intro_phil_stat_2022

MSc-level 1h introduction to the philosophy of statistics

lmisc

lnalborczyk.github.io

Source code of my website

maskr

meeg_decoding

Open source tools for M/EEG multivariate pattern analyses (aka decoding)

meta_distance_perception

Action Effects on Visual Perception of Distances: A Multilevel Bayesian Meta-Analysis

momimi

Models of Motor Inhibition during Motor Imagery

motor_imagery_inhibition_model

A simplistic model of motor inhibition during motor imagery

motor_inhibition_covert_speech

Perspective paper by Nalborczyk et al. (2022)

orthographic_graphomotor_constraints

Materials for the paper "Interaction between orthographic and graphomotor constraints in learning to write" (Danna et al., 2022).

phd_thesis

Available online: https://www.barelysignificant.com/phd_thesis/

pragmatism_commentary

Pragmatism should not be a substitute for statistical literacy, a commentary on Albers, Kiers, and van Ravenzwaaij (2018).

resources

A list of resources for students, collaborators, and myself.

shiny

Various Shiny applications.

shiny_ddm

An interactive visualisation of the Wiener drift diffusion model

shiny_inner_speech

A shiny app to explore inner speech auditory percepts

surface_emg_cnn

Reanalysing the data from Nalborczyk et al. (2020, PLOS ONE) using deep neural networks.