Provides a lightweight R interface to the Interactive Brokers (IBKR)
    Client Portal REST API. Functions cover session management, account and portfolio
    queries, market data retrieval, and order placement and cancellation. Requires a
    locally running IBKR Client Portal Gateway.

Implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) <https://ifoa-adswp.github.io/IBLM/reference/figures/iblm_paper.pdf>.

Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) 
  and methods to create new ones, particularly for population dynamics models (reproduction, 
  mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed.

Authentication can be the most difficult part about
  working with a new API. 'ibmAcousticR' facilitates making a
  connection to the 'IBM Acoustic' email campaign management API
  and executing various queries. The 'IBM Acoustic' API 
  documentation is available at
  <https://developer.ibm.com/customer-engagement/docs/>. This
  package is not supported by 'IBM'.

It provides a generic set of tools for initializing a synthetic
         population with each individual in specific disease states, and
         making transitions between those disease states according to the rates
         calculated on each timestep. The new version 1.0.0 has C++ code 
         integration to make the functions run faster. It has also a higher level
         function to actually run the transitions for the number of timesteps
         that users specify. Additional functions will follow for changing
         attributes on demographic, health belief and movement.

Functionality required to efficiently use R with IBM(R) Db2(R)
        Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in
        conjunction with IBM Db2 Analytics Accelerator for z/OS.
        Many basic and complex R operations are pushed down into the database, 
        which removes the main memory boundary of R and allows to make full 
        use of parallel processing in the underlying database.
        For executing R-functions in a multi-node environment in parallel the idaTApply() function
        requires the 'SparkR' package (<https://spark.apache.org/docs/latest/sparkr.html>).
        The optional 'ggplot2' package is needed for the plot.idaLm() function only.