Description: Provides flexible but lightweight logging facilities for R scripts.
Supports priority levels for logs and messages, flagging messages, capturing
script output, switching logs, and logging to files or connections.

Description: Core functionality for working with vectors (numeric, integer,
logical and character) that are too large to keep in memory. The vectors are
kept (partially) on disk using memory mapping. This package contains the
basic functionality for working with these memory mapped vectors (e.g.
creating, indexing, ordering and sorting) and provides C++ headers which can
be used by other packages to extend the functionality provided in this

Title: Latent Variable Models for Networks
Description: Latent variable models for network data using fast inferential

Authors@R: person(given = "Isabella", family = "Gollini", role = c("aut","cre"), email = "igollini.stats@gmail.com")
Depends: R (>= 3.1.1), MASS, ergm, network
Imports: igraph, grDevices, graphics, ellipse, stats, utils

Depends: R (>= 3.1.1), MASS, ergm, network, ellipse

procedures. For more information please visit: <http://igollini.github.io/lvm4net/>.
Authors@R: person(given = "Isabella", family = "Gollini", role = c("aut","cre"), email = "igollini.stats@gmail.com", comment = c(ORCID = "0000-0002-7738-5688"))
Depends: R (>= 3.5), MASS, ergm, network
Imports: igraph, grDevices, graphics, ellipse, stats, utils, glmmML,
Suggests: knitr, rmarkdown, manet, colorspace
Author: Isabella Gollini [aut, cre] (<https://orcid.org/0000-0002-7738-5688>)

Title: Stochastic EM Algorithms for Latent Variable Models with a
person("Siliang", "Zhang", , "zhangsiliang123@gmail.com", c("aut", "cre")),
person("Yunxiao", "Chen", , role = "aut"),
person("Jorge", "Nocedal", , role = "cph"),
person("Naoaki", "Okazaki", , role= "cph")
Description: Provides stochastic EM algorithms for latent variable models
with a high-dimensional latent space. So far, we provide functions for confirmatory item
factor analysis based on the multidimensional two parameter logistic (M2PL) model and the
generalized multidimensional partial credit model. These functions scale well for problems
with many latent traits (e.g., thirty or even more) and are virtually tuning-free.
The computation is facilitated by multiprocessing 'OpenMP' API.
Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale
Full-information Item Factor Analysis. British Journal of Mathematical and Statistical
Packaged: 2018-12-19 05:34:41 UTC; siliangzhang