 Hello everybody, today I'm going to talk to you about my R-Package for Computing Implicit Measures course. So what is implicit assessment in psychology? It is assessment of people attitudes without directly asking them, but by inferring them from the speed with which respondents are performing categorization tasks into contrasting conditions, say condition A and condition B. The two most commonly employed measures are the Implicit Association Test, which can answer to the question, how much do you like coke or Pepsi, and the Single Category Implicit Association Test, which can answer to the question, how much do you like coke or how much do you like Pepsi. So the IAT provides a comparative measure of the preference between two objects, while the Single Category can provide a measure that is absolute toward just one object. So the IAT and Single Category effects, which are the differences in the respondents' performance between the two associative conditions, are usually expressed by using the so-called Discord, which is simply computed as the difference between the average response times in the two conditions standardized by using the pool standard deviation. The competition per se is rather easy, but the steps that have to be undertaken to clean and prepare the data set make it an ever-prone procedure, which can raise, of course, replicability issues. That's why I decided to grade this package to automate the cleaning and computation procedure and hence obtaining more applicable results. Implicit measures is available on Cran, so you can download it from Cran and upload it in your R. Implicit measures comes with a toy data set, row data, that you can use to familiarize yourself with its functions. So we have measured specific functions for computing, cleaning the data for the IAT and the Single Category, a function for compute the IAT reliability, and since there are six different algorithms for computing the IAT Discord, we also have a function that simultaneously compute and plot all the available algorithms. While for the Single Category, we have a function that plots together the results from two different Single Categories. Finally, we also have common functions for both the IAT and Single Category results, which summarize the results of these measures in descriptive tables or plots their distributions. So finally, last but not least, thank you so much for your attention and this QR code will redirect you to the Cran page of my package. Thanks again for your attention and please contact me if you have any further questions.