# Ross Ihaka

#### 9 packages on CRAN

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

The exponential integrals E_1(x), E_2(x), E_n(x) and Ei(x), and the incomplete gamma function G(a, x) defined for negative values of its first argument. The package also gives easy access to the underlying C routines through an API; see the package vignette for details. A test package included in sub-directory example_API provides an implementation. C routines derived from the GNU Scientific Library <https://www.gnu.org/software/gsl/>.

We provide an outlier robust alternative of the function ets() in the 'forecast' package of Hyndman and Khandakar (2008) <DOI:10.18637/jss.v027.i03>. For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is described in more detail in Crevits and Croux (2016) <DOI:10.13140/RG.2.2.11791.18080>.

Facilities for running simulations from ordinary differential equation (ODE) models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The VODE and LSODA are in the public domain. The information is available in the inst/COPYRIGHTS.

An implementation of maximum entropy sampling for spatial data is provided. An exact branch-and-bound algorithm as well as greedy and dual greedy heuristics are included.

Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals.

Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to <doi:10.1080/01621459.2017.1409122> for more detail.

Implementation of Jones (2007) <doi:10.1016/j.cct.2007.02.008> , Tournoux-Facon (2011) <doi:10.1002/sim.4148> and Parashar (2016) <doi:10.1002/pst.1742> designs.