Title: Factor Model Estimation Using Proxy Variables
Description: Functions to estimate a factor model using discrete and continuous proxy variables. The function 'dproxyme' estimates a factor model of discrete proxy variables using an EM algorithm (Dempster, Laird, Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>; Hu (2008) <doi:10.1016/j.jeconom.2007.12.001>; Hu(2017) <doi:10.1016/j.jeconom.2017.06.002> ). The function 'cproxyme' estimates a linear factor model (Cunha, Heckman, and Schennach (2010) <doi:10.3982/ECTA6551>).
Imports: dplyr, nnet, pracma, stats, utils, gtools

Author: Dave Armstrong (Department of Political Science, UW-Milwaukee)
Description: Calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models

Title: Graphical Presentation of Simple Contrasts
Description: Methods to calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models.

Title: Bayesian Estimation of (Sparse) Latent Factor Stochastic
person("Gregor", "Kastner", role = c("aut", "cre"), email = "gregor.kastner@aau.at", comment = c(ORCID = "0000-0002-8237-8271")),
person("Darjus", "Hosszejni", role = c("ctb"), email = "darjus.hosszejni@wu.ac.at", comment = c(ORCID = "0000-0002-3803-691X")))
Description: Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Imports: GIGrvg (>= 0.4), Rcpp (>= 1.0.0), corrplot, methods,
grDevices, graphics, stats, utils, stochvol (>= 3.0.0)
Suggests: LSD (>= 4.0-0), coda (>= 0.19-2), knitr, RColorBrewer,
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.900), stochvol
Author: Gregor Kastner [aut, cre] (<https://orcid.org/0000-0002-8237-8271>),

grDevices, graphics, stats, utils, stochvol (>= 3.0.2)
Packaged: 2021-02-09 09:25:23 UTC; grkastner

Authors@R: person("Gregor", "Kastner", role = c("aut", "cre"),
Description: Markov chain Monte Carlo (MCMC) sampler for fully Bayesian
estimation of latent factor stochastic volatility models.
Sparsity can be achieved through the usage of Normal-Gamma priors
corrplot, methods, grDevices, graphics, stats, utils
LinkingTo: Rcpp, RcppArmadillo (>= 0.4), stochvol