R.rsp, knitr, MCMCpack, rmarkdown, RColorBrewer, reshape2

Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data
(i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2017) <doi:10.13140/RG.2.2.34599.96164>.
2018-01-17 13:29:59 UTC; Pedro Albuquerque

2018-03-06 18:51:53 UTC; Pedro Albuquerque

knitr, MCMCpack, rmarkdown, RColorBrewer, reshape2
2018-07-13 13:38:47 UTC; Pedro Albuquerque

Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data
(i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2019) <doi:10.1016/j.knosys.2018.10.024>.
2018-12-13 11:53:36 UTC; Pedro Albuquerque

Bayesian Exponential Smoothing Models with Trend Modifications
c(
person("Slawek", "Smyl", email = "slaweks@hotmail.co.uk", role = "aut"),
person("Christoph", "Bergmeir", email = "christoph.bergmeir@monash.edu", role = c("aut", "cre")),
person("Erwin", "Wibowo", email = "rwinwibowo@gmail.com", role = "aut"),
person("To Wang", "Ng", email = "edwinnglabs@gmail.com", role = "aut"),
person("Trustees of Columbia University", "", role = c("cph"), comment= "tools/make_cpp.R, R/stanmodels.R"))
An implementation of a number of Global Trend models for time series forecasting
that are Bayesian generalizations and extensions of some Exponential Smoothing models.
The main differences/additions include 1) nonlinear global trend, 2) Student-t error
distribution, and 3) a function for the error size, so heteroscedasticity. The methods
are particularly useful for short time series. When tested on the well-known M3 dataset,
they are able to outperform all classical time series algorithms. The models are fitted
with MCMC using the 'rstan' package.
R (>= 3.4.0), Rcpp (>= 0.12.0), methods, rstantools, forecast
Slawek Smyl [aut],
Christoph Bergmeir [aut, cre],
Erwin Wibowo [aut],
To Wang Ng [aut],
Trustees of Columbia University [cph] (tools/make_cpp.R,
R/stanmodels.R)

Ensemble Empirical Mode Decomposition (EEMD) and Its Complete
Variant (CEEMDAN)
c(person(given = "Jouni", family = "Helske", role = c("aut","cre"),
comment = "R interface",email = "jouni.helske@jyu.fi"), person(given =
"Perttu", family = "Luukko", role = "aut", comment = "Original libeemd C
library"))
An R interface for C library libeemd for performing the ensemble
empirical mode decomposition (EEMD), its complete variant (CEEMDAN) or the
regular empirical mode decomposition (EMD).
Jouni Helske [aut, cre] (R interface),
Perttu Luukko [aut] (Original libeemd C library)