stringr (>= 1.2.0), tibble, tidyr, memoise, rlang, progress, qs

Contains some procedures for latent variable modelling with a
The LAM package contains mean and covariance structure modelling
for multivariate normally distributed data ('mlnormal'),
a general Metropolis-Hastings algorithm ('amh') and penalized
Imports: CDM, coda, graphics, Rcpp, sirt (>= 2.0), stats, utils

The 'LAM' package contains mean and covariance structure modelling
for multivariate normally distributed data (mlnormal()),
a general Metropolis-Hastings algorithm (amh()) and penalized
Imports: CDM, coda, graphics, Rcpp, sirt (>= 2.0), stats, TAM (>= 2.8),
Suggests: lavaan, lme4, MASS, STARTS (>= 0.2)

Includes some procedures for latent variable modeling with a
for multivariate normally distributed data (mlnormal(); Longford, 1987;
<doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm
penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland,
Imports: CDM, coda, graphics, MASS, numDeriv, Rcpp, sirt (>= 2.0),

Imports: CDM, graphics, Rcpp, sirt, stats, utils
Suggests: coda, expm, MASS, numDeriv, STARTS, TAM

Suggests: coda, expm, MASS, numDeriv, TAM

Title: Liquid Association for Network Dynamics Detection
Author: Shangzhao Qiu, Yan Yan, Tianwei Yu
Description: Using Liquid Association for Network Dynamics Detection.
Imports: igraph, fdrtool, foreach, doParallel, GOstats, GOSemSim,
GGally, intergraph, ggplot2, modeest, Matrix, Rcpp
Packaged: 2015-10-06 04:52:22 UTC; danielqiu

GGally, intergraph, ggplot2, modeest, Matrix, Rcpp, mvtnorm,
Packaged: 2016-09-30 23:03:19 UTC; danielqiu

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

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