Survival Analysis

Survival analysis is a statistical approach for estimating the timing of events. This series of tutorials demonstrates how to conduct survival analysis specifically on observational data (i.e., video recordings of participant behaviour in situ), although these resources will also be applicable to other types of time-to-event data.

Intensive Longitudinal Data: Analysis of Experience Sampling and EMA Data

Intensive longitudinal data are often collected using ecological momenary assessment (EMA), expereince sampling (ESM), daily diary, ambulatory assessment, and related designs. Chronicling our experience working with data from such studies, we are building a repository of scripts and tutorials that researchers may find useful during analysis of such data. Our notes follow courses and workshops we teach on the topic (Appied Longitudinal Data Analysis), and the excellent text on Intensive Longitudinal Data by Bolger and Laurenceau (2013). 

What’s for dynr: A package for linear and nonlinear dynamic modeling in R

The past several decades have seen the rise of intensive longitudinal data (e.g. via ecological momentary assessments) and the resulting dynamic modeling methods in social and behavioral sciences. To make the estimation of these models more accessible to researchers, we have created an R package that is based on a novel and efficient state-space estimation algorithm in C.