Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.
Longitudinal data analysis using generalized linear models
Multilevel Models for Categorical Data using SAS ® PROC GLIMMIX: The Basics
Generalized Linear Models for count time series
Sufficient Sample Sizes for Multilevel Modeling
Regression Analysis of Longitudinal Data in the Presence of Informative Observation and Censoring Times
Missing Not at Random Models for Latent Growth Curve Analyses
Dynamic Mixture Models for Multiple Time-Series
Approximate Inference in Generalized Linear Mixed Models
Modeling Multilevel Data Structures
Generalized Additive Models
Nonparametric and semiparametric methods in R
An Intermediate Guide to Estimating Multilevel Models for Categorical Data using SAS ® PROC GLIMMIX
Conducting Meta-Analyses in R with the metafor Package
New Parsimonious Multivariate Spatial Model: Spatial Envelope
Sample size and power determination in joint modeling of longitudinal and survival data
Estimating within-study covariances in multivariate meta-analysis with multiple outcomes
Multilevel Models for Ordinal and Nominal Variables
fitting linear mixed effects models using lme 4 arxiv
Simultaneous Modelling of Covariance Matrices: GLM, Bayesian and Nonparametric Perspectives
Beta Regression in R
churn reduction in the wireless industry .
large ‐ scale correlation of accounts across social networks .
Random Effects Models for Longitudinal Data
Agent-Based Distributed Intrusion Alert System
Wireless Cloud Networks for the Factory of Things: Connectivity Modeling and Layout Design