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The NSF grant awards can be easily searched via NSF website. Check Active Awards and type “Bayesian & Cognitive Diagnosis” into the search box. Two things I’m really interested in: (1) Who as Principal Investigator (PI) obtained most NSF awards?

This is an example of how to web scrape grants of active research in college’s official website. Please follow the academia institute’s website robotes rules. library(XML) library(stringr) library(rvest) library(tidyverse) library(kableExtra) Web Scrapping topics = rep(NA, 84) fundings = rep(NA, 84) iter = 0 for (page in 1:9) { ## parent webpage scrape_url <- paste0('http://XXXXXXXXXXXXXXXXXXXXXX', page) html_form_page <- read_html(scrape_url) ## find the child web page containing projects' name, total award, topic etc.

Table of Contents Global Power Local Power Conclusion Discussion The statistical power of a hypothesis test is the probability of detecting an effect, if there’s a true effect presented to detect.

Resource Michael Clark has a great workshop in his blog introducing distill package. I suggest readers check it out first. Requirement Following software is necessary to replicate the steps in this post:

This blog build GD algorithm via Julia programming language

Abstract This study explored school- and teacher-level factors associated with a higher probability of teachers’ implementation of student-centered instructional practices. These practices include adaptive instruction and active teaching strategies that support learning enhanced by information communication technology (ICT), collaborative small-group learning, and project-based learning.

This tutorial aims to explore various types of tools of visualizing the process data.

Abstract Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by the location of a ML-based estimate to the predictive distribution of a statistic for a model.

A introduction about gt package is here knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, fig.align = "default", eval = TRUE) library(gt) ## Warning: package 'gt' was built under R version 4.