Contains some procedures for latent variable modelling with a
particular focus on multilevel data.
The 'LAM' package contains mean and covariance structure modelling
for multivariate normally distributed data (mlnormal()),
a general Metropolis-Hastings algorithm (amh()) and penalized
maximum likelihood estimation (pmle()).
CDM, coda, graphics, Rcpp, sirt (>= 2.0), stats, TAM (>= 2.8),
utils

Includes some procedures for latent variable modeling with a
particular focus on multilevel data.
The 'LAM' package contains mean and covariance structure modelling
for multivariate normally distributed data (mlnormal(); Longford, 1987;
<doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm
(amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and
penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland,
2014; <doi:10.1093/aje/kwt245>).
CDM, coda, graphics, MASS, numDeriv, Rcpp, sirt (>= 2.0),
stats, TAM (>= 2.8), utils

Liquid Association for Network Dynamics Detection
Using Liquid Association for Network Dynamics Detection.
igraph, fdrtool, foreach, doParallel, GOstats, GOSemSim,
GGally, intergraph, ggplot2, modeest, Matrix, Rcpp

igraph, fdrtool, foreach, doParallel, GOstats, GOSemSim,
GGally, intergraph, ggplot2, modeest, Matrix, Rcpp, mvtnorm,
pROC

LARF: Local Average Response Functions for Estimating Treatment
Effects
Weihua An and Xuefu Wang, Indiana University Bloomington
Provides instrumental variable estimation of treatment
effects when both the treatment variable and instrumental
variable are binary. Applicable to both binary and continuous
outcomes.

Provides instrumental variable estimation of treatment effects when the treatment variable is binary. Applicable to both binary and continuous outcomes.

Local Average Response Functions for Instrumental Variable
Estimation of Treatment Effects
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.