person("Ben", "Bond-Lamberty", email = "bondlamberty@pnnl.gov", role = c("aut", "cre"))
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.

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.

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
package.

Latent variable models for network data using fast inferential
procedures.

person(given = "Isabella", family = "Gollini", role = c("aut","cre"), email = "igollini.stats@gmail.com")
igraph, grDevices, graphics, ellipse, stats, utils

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

Latent variable models for network data using fast inferential
procedures. For more information please visit: <http://igollini.github.io/lvm4net/>.
person(given = "Isabella", family = "Gollini", role = c("aut","cre"), email = "igollini.stats@gmail.com", comment = c(ORCID = "0000-0002-7738-5688"))
igraph, grDevices, graphics, ellipse, stats, utils, glmmML,
mvtnorm, corpcor
Isabella Gollini [aut, cre] (<https://orcid.org/0000-0002-7738-5688>)

Stochastic EM Algorithms for Latent Variable Models with a
High-Dimensional Latent Space
c(
person("Siliang", "Zhang", , "zhangsiliang123@gmail.com", c("aut", "cre")),
person("Yunxiao", "Chen", , role = "aut"),
person("Jorge", "Nocedal", , role = "cph"),
person("Naoaki", "Okazaki", , role= "cph")
)
Siliang Zhang [aut, cre],
Yunxiao Chen [aut],
Jorge Nocedal [cph],
Naoaki Okazaki [cph]
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.
For more information, please refer to:
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
Psychology. <doi:10.1111/bmsp.12153>.