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Packaged: 2020-11-05 21:46:30 UTC; teamcity-agent

Packaged: 2020-12-17 18:05:53 UTC; teamcity-agent

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Title: Bayesian LDA for Mixed-Membership Clustering Analysis
Author: Pedro Albuquerque and Denis Valle and Daijiang Li
Description: Describes the Bayesian LDA model for mixed-membership clustering based on different types of data (i.e., Multinomial, Bernoulli, and Binomial entries).
Packaged: 2017-03-16 14:01:05 UTC; p.albuquerque

Description: Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data (i.e., Multinomial, Bernoulli, and Binomial entries).
Suggests: knitr, rmarkdown, RColorBrewer, reshape2
Packaged: 2017-04-13 12:42:18 UTC; p.albuquerque

Suggests: R.rsp, knitr, MCMCpack, rmarkdown, RColorBrewer, reshape2
Packaged: 2017-06-02 18:34:17 UTC; p.albuquerque

Packaged: 2017-07-19 17:15:24 UTC; p.albuquerque

Packaged: 2017-07-26 12:12:18 UTC; p.albuquerque

Description: 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>.
Packaged: 2018-01-17 13:29:59 UTC; Pedro Albuquerque

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

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

(i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2019) <doi:10.1016/j.knosys.2018.10.024>.
Packaged: 2018-12-13 11:53:36 UTC; Pedro Albuquerque

Title: Bayesian Exponential Smoothing Models with Trend Modifications
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"))
Description: 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
Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods, rstantools, forecast
Trustees of Columbia University [cph] (tools/make_cpp.R,