Education 401D  Spring 2015
    Multilevel Modeling Using R

  EDUC 401D: Multilevel Modeling Using R
Multilevel data analysis examples using R. 
Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data, 
three-level analyses. For more information, see course website: http://web.stanford.edu/~rag/ed401d/
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Rogosa, D. (PI) 

*Preliminary* Course Schedule
Five (2hr) mtgs M 3:15 - 5:05 on April 6 13 20 27, May 11  Building 160, Rm 314
 week 1. Introduction: Analyses for two-level nested data  and for longitudinal Data (growth curves)          
 week 2. Additional two-level models for observational data (High School and Beyond ex) and experimental designs.
 week 3. Beyond basic linear models: generalized linear mixed models for counts and categorical outcomes; nonlinear functional forms
 week 4. Three-level analyses (nested data and longitudinal data)    
 week 5. Student presentations of multilevel data analyses  

amer -- Additive mixed models with lme4
arm -- Data Analysis Using Regression and Multilevel/Hierarchical Models
coxme -- Mixed Effects Cox Models
gamm4 -- Generalized additive mixed models using mgcv and lme4
GLMMarp -- Generalized Linear Multilevel Model with AR(p) Errors Package
glmmAK -- Generalized Linear Mixed Models
heavy -- Estimation in the linear mixed model using heavy-tailed distributions
hglm -- hglm is used to fit hierarchical generalized linear models
HGLMMM -- Hierarchical Generalized Linear Models
influence.ME -- Tools for detecting influential data in mixed effects models
kinship -- mixed-effects Cox models, sparse matrices, and modeling
data from large pedigrees
lme4 -- Linear mixed-effects models using S4 classes
lmeSplines -- lmeSplines
lmec -- Linear Mixed-Effects Models with Censored Responses
lmm -- Linear mixed models
longRPart -- Recursive partitioning of longitudinal data using mixed-effects models
MASS -- Main Package of Venables and Ripley's MASS (see function glmmPQL)
MCMCglmm -- MCMC Generalised Linear Mixed Models
MEMSS -- Data sets from Mixed-effects Models in S
mlmRev -- Examples from Multilevel Modelling Software Review
multilevel -- Multilevel Functions
nlme -- Linear and Nonlinear Mixed Effects Models
nlmeODE -- Non-linear mixed-effects modelling in nlme using differential equations
npde -- Normalised prediction distribution errors for nonlinear mixed-effect models
PSM -- Non-Linear Mixed-Effects modelling using Stochastic Differential Equations
pamm -- Power analysis for random effects in mixed models
pedigreemm -- Pedigree-based mixed-effects models
phmm -- Proportional Hazards Mixed-effects Model (PHMM)
RLRsim -- Exact (Restricted) Likelihood Ratio tests for mixed and additive models
glmmADMB -- Mixed models for discrete data in R
OpenMx -- Structural Equation Modeling (can handle multilevel problems)
rjags - interface for JAGS for Bayesian models
rstan - interface for Stan for Bayesian models
glmmBUGS -- Generalised Linear Mixed Models and Spatial Models with BUGS