Title: An Event-Based Mechanism for 'Shiny'
Description: An event-Based framework for building 'Shiny' apps.
Instead of relying on standard 'Shiny' reactive objects, this
package allow to relying on a lighter set of triggers, so that
reactive contexts can be invalidated with more control.

Title: Fitting and Forecasting Gegenbauer ARMA Time Series Models
Authors@R: person("Richard", "Hunt", email = "maint@huntemail.id.au",
Description: Methods for estimating univariate long memory-seasonal/cyclical
Gegenbauer time series processes. See for example (2018) <doi:10.1214/18-STS649>.
Refer to the vignette for details of fitting these processes.
Imports: Rsolnp, pracma, signal, zoo, lubridate, crayon, utils, nloptr,
Suggests: longmemo, yardstick, tidyverse, testthat, knitr, rmarkdown

Description: Methods for estimating long memory-seasonal/cyclical Gegenbauer univariate time series processes. See for example (2018) <doi:10.1214/18-STS649>.
Imports: assertthat, zoo, forecast, lubridate, FKF, signal, pracma,
nloptr, Rsolnp, ggplot2, Rdpack (>= 0.7)
Suggests: longmemo, tidyverse, BB, GA, pso, dfoptim, testthat, knitr,

Imports: Rsolnp, ggplot2, pracma, signal, zoo, lubridate, FKF, nloptr,
Suggests: longmemo, yardstick, tidyverse, BB, GA, pso, dfoptim,

Title: Generalized Array Arithmetic for Ragged Arrays with Named
Description: Organize a so-called ragged array as generalized arrays, which
is simply an array with sub-dimensions denoting the subdivision of
dimensions (grouping of members within dimensions).
By the margins (names of dimensions and sub-dimensions) in generalized
arrays, operators and utility functions provided in this package
automatically match the margins, doing map-reduce style calculation
along margins.  Generalized arrays are also cooperative to R's native

doing map-reduce style parallel computation

Title: Genetic Algorithm (GA) for Variable Selection from
Description: Provides a genetic algorithm for finding variable
subsets in high dimensional data with high prediction performance. The
genetic algorithm can use ordinary least squares (OLS) regression models or
partial least squares (PLS) regression models to evaluate the prediction
power of variable subsets. By supporting different cross-validation
schemes, the user can fine-tune the tradeoff between speed and quality of
LinkingTo: Rcpp (>= 0.10.5), RcppArmadillo (>= 0.9.800.4)
'evaluate.R' 'fitness.R' 'genAlg.R' 'getEvalFun.R' 'subsets.R'