Ordinary and modified statistics for symmetrical linear
    regression models with small samples. The supported ordinary
    statistics include Wald, score, likelihood ratio and gradient. The
    modified statistics include score, likelihood ratio and gradient.
    Diagnostic tools associated with the fitted model are implemented. For
    more details see Medeiros and Ferrari (2017) <DOI:10.1111/stan.12107>.

With given inputs that include number of points, discrete design space, a measure of skewness, models and parameter value, this package calculates the objective value, optimal designs and plot the equivalence theory under A- and D-optimal criteria under the second-order Least squares estimator. This package is based on the paper "Properties of optimal regression designs under the second-order least squares estimator" by Chi-Kuang Yeh and Julie Zhou (2021) <doi:10.1007/s00362-018-01076-6>.

Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.

User-friendly functions which parse output of
 command line programs used to query Slurm.
 Morris A. Jette and Tim Wickberg (2023)
 <doi:10.1007/978-3-031-43943-8_1> describe Slurm in detail.

'Slurm', Simple Linux Utility for Resource Management
          <https://slurm.schedmd.com/>, is a popular 'Linux' based software used to 
          schedule jobs in 'HPC' (High Performance Computing) clusters. This R package
          provides a specialized lightweight wrapper of 'Slurm' with a syntax similar to
          that found in the 'parallel' R package. The package also includes a method for
          creating socket cluster objects spanning multiple nodes that can be used with
          the 'parallel' package.

This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.