person(given = "Ryan",
family = "Price",
role = c("cre", "aut"),
email = "ryapric@gmail.com")

An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary
log-writing interfaces. It provides a set of wrappings for base R's
message(), warning(), and stop() functions that maintain identical
functionality, but also log the handler message to an 'ndjson' log file.
'loggit' also exports its internal 'loggit()' function for powerful and
configurable custom logging. No change in existing code is necessary to use
this package, and should only require additions to fully leverage the power
of the logging system. 'loggit' also provides a log reader for reading an
'ndjson' log file into a data frame, log rotation, and live echo of the
'ndjson' log messages to terminal 'stdout' for log capture by external
systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines,
modeling work flows, and more. Please see the vignettes for detailed example
use cases.

Provides a set of methods that learn time-varying graphical models based on data measured over a temporal grid. The underlying statistical model is motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stocks interact with each other and how such interactions change over time. The time-varying graphical models are estimated under the assumption that the graph topology changes gradually over time. For more details on estimating time-varying graphical models, please refer to: Yang, J. & Peng, J. (2018) <arXiv:1804.03811>.
sparseMVN, matrixcalc, XML, RCurl, quantmod

Binning Variables to Use in Logistic Regression
Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.
partykit, doParallel, data.table, foreach, iterators, parallel