**Kinetic perimetry is used to quantify visual field size/sensitivity. Clinically, perimetry can be used to diagnose and monitor ophthalmic and neuro-ophthalmic disease. Normative data are integral to the interpretation of these findings. However, there are few computational developments that allow clinicians to collect and analyze normative data from kinetic perimeters. In this article we describe an approach to fitting kinetic responses using linear quantile mixed models. Analogously to traditional linear mixed-effects models for the mean, linear quantile mixed models account for repeated measurements taken from the same individual, but differently from linear mixed-effects models, they are more flexible as they require weaker distributional assumptions and allow for quantile-specific inference. Our approach improves on parametric alternatives based on normal assumptions. We introduce the R package kineticF, a freely available and open-access resource for the analysis of perimetry data. Our proposed approach can be used to analyze normative data from further studies.**

*r*,

*θ*) defining points along meridians. Due to the cyclical nature of the isopters (Figure 1A), we used harmonic linear predictors in our regression models. For these data, a simple model with sine and cosine terms of periods

*π*and

*π*/2 could be specified as follows: where

*r*is the isopter value corresponding to meridian

*θ*and the

*β*s are the model's parameters. Of course, Model 1 can include interactions between the sine and cosine terms, or higher frequency harmonics, and be adjusted for confounders such as age and sex. Typically, the error term

*ε*is assumed to be normal, with zero mean and constant variance.

**Figure 1**

**Figure 1**

*p*th centile of the conditional distribution of

*r*from Equation 1 is defined as where

*β*

_{0,}

*is an intercept that depends on the quantile*

_{p}*p*. However, note that the slopes in Equation 2 are constrained by the normal model to be the same for all quantiles. This means that all the individuals in the sample are assumed to have the same distribution, except for a shift

*β*

_{0,}

*, which is determined by the normal distribution.*

_{p}*p*. Thus, the location, scale, and shape of the error term

*ε*are not determined by a theoretical distribution (e.g., normal) but are modeled empirically.

*, 38 (4), 189–198.*

*Neuro-Ophthalmology**, 169, 1–64.*

*Acta Ophthalmologic Supplementum**, 57 (13), 1–29.*

*Journal of Statistical Software**, 24 (3), 461–479.*

*Statistics and Computing**, 43 (1), 118–132.*

*Canadian Journal of Statistics**. London: Chapman and Hall.*

*Nonparametric regression and generalized linear models: A roughness penalty approach**. New York, NY: Cambridge University Press.*

*Quantile regression**, 33 (2), 188–229.*

*Sociological Methods & Research**, 216 (6), 406–408.*

*Ophthalmologica**. Available at http://cran.r-project.org/web/packages/kineticF/index.html*

*KineticF: Framework for the analysis of kinetic visual field data**, 122 (8), 1711–1717.*

*Ophthalmology**, 10 (6), e0130895.*

*PLoS One**, 28 (6), 314–319.*

*Journal of Pediatric Ophthalmology & Strabismus**. Vienna, Austria: R Foundation for Statistical Computing. Available at http://www.R-project.org*

*R: A language and environment for statistical computing**, 114 (6), 1065–1072.*

*Ophthalmology**, 248, 1493–1500.*

*Graefe's Archive for Clinical and Experimental Ophthalmology**, 28 (3), 151–154.*

*Journal of Pediatric Ophthalmology & Strabismus*