Design and Analysis of 2x2 Factorial Trial
Used for the design and analysis of a 2x2 factorial trial for
a time-to-event endpoint.  Performs power calculations and significance
testing.  Important reference papers include
Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609>
Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507>
Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2019)
<https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al%20Factorial.pdf>.

Design and Analysis of a 2x2 Factorial Trial
Used for the design and analysis of a 2x2 factorial trial for
a time-to-event endpoint.  It performs power calculations and significance
testing as well as providing estimates of the relevant hazard ratios and the
corresponding 95% confidence intervals.  Important reference papers include
Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609>
Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507>
Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020)
<https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.

Factor Model Estimation Using Proxy Variables
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>).
dplyr, nnet, pracma, stats, utils, gtools

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

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

Bayesian Estimation of (Sparse) Latent Factor Stochastic
Volatility Models
c(
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")))
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>.
GIGrvg (>= 0.4), Rcpp (>= 1.0.0), corrplot, methods,
grDevices, graphics, stats, utils, stochvol (>= 3.0.0)
LSD (>= 4.0-0), coda (>= 0.19-2), knitr, RColorBrewer,
testthat (>= 2.1.0)
Rcpp, RcppArmadillo (>= 0.9.900), stochvol
Gregor Kastner [aut, cre] (<https://orcid.org/0000-0002-8237-8271>),
Darjus Hosszejni [ctb] (<https://orcid.org/0000-0002-3803-691X>)