One-Step Functional Generalized Estimating Equations

Analysis guide for applying sequential excursion effects methods described in: 1. Loewinger, G.*, Levis, A. W.*, & Pereira, F. (2026+). Nonparametric causal inference for optogenetics: sequential excursion effects for dynamic regimes. Journal of the American Statistical Association. 2. Levis, A. W.*, Loewinger, G.*, & Pereira, F. (2024). Causal inference in the closed-loop: Marginal structural models for sequential excursion effects. Advances in Neural Information Processing Systems, 37, 109123-109151.

fastFGEE R package Vignette: One-Step Functional Generalized Estimating Equations

Vignette for fast Functional Generalized Estimating Equations for large datasets.

We show how to use FLMM to test how signal–covariate associations change across time.

FLMM akin to ANOVA

Using FLMM to analyze associations between the signal and continuous variables akin to a correlation.

Here we analyze if there are significant changes in mean signal magnitude between two periods of the same trial (baseline vs. cue-period) within a single trial type (CS+ trials).

An introduction to analyzing fiber photometry data with FLMM. We start with a single binary covariate akin to the FLMM version of a paired t-test.

fastFMM: Fast Functional Mixed Models using Fast Univariate Inference (FUI)

Tutorial for how to download and use sMTL, the Sparse Multi-Task Learning R Package.