| SGF |
2019 |
Introduction to Nonlinear Mixed-Effects Models of Longitudinal Data |
|
source |
6 |
| WUSS |
2018 |
A latent variable location scale model for intensive longitudinal data |
|
source |
3 |
| WUSS |
2018 |
Modeling Heterogeneous Within-Subject Variability |
Madeline Craft |
source |
8 |
| WUSS |
2015 |
Approximation methods and quadrature points in PROC NLMIXED: A simulation study using structured latent curve models |
Nate Smith |
source |
10 |
| WUSS |
2015 |
Pattern-Mixture Random-Effects Models for Non-Ignorable Missing Data in Longitudinal Studies |
Ricardo Villarreal |
source |
3 |
| WUSS |
2014 |
Options in Estimating Nonlinear Mixed Models: Quadrature points and Approximation Methods |
Nate Smith |
source |
7 |
| WUSS |
2013 |
User-Specified Likelihood Expressions Using NLMIXED and the GENERAL Statement |
Melissa McTernan |
source |
4 |
| WUSS |
2013 |
Using PROC NLMIXED to Fit Correlated Residual Error Structures in Nonlinear Mixed Models |
Jeffrey Harring |
source |
5 |
| WUSS |
2012 |
Sensitivity analysis of nonlinear mixed-effects models for longitudinal data that areincomplete |
|
source |
6 |