* Create an SVG-Based Mathematical Formula* (

**eqn2svg**)

If you have a mathematical formula and the need to have that formula in the form of scalable vector graphics (SVG), you’ll be delighted by what ‘eqn2svg’ will let you do. The incoming LaTeX math formula will be nicely converted to SVG tags. And you can use that code wherever SVGs are accepted.

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**Yang and Prentice Model with Piecewise Exponential Baseline Distribution****YPPE**)

Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution curves. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.

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**Non-Parametric Sampling with Parallel Monte Carlo****PosteriorBootstrap**)

An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), ‘Nonparametric learning from Bayesian models with randomized objective functions’ <arXiv:1806.11544>.

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**Preprocessing Operators and Pipelines for ‘mlr3’****mlr3pipelines**)

Dataflow programming toolkit that enriches ‘mlr3’ with a diverse set of pipelining operators (‘PipeOps’) that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as ‘mlr3’ ‘Learners’ and can therefore be resampled, benchmarked, and tuned.

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**Single-Index Models with a Surface-Link****simsl**)

An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2019) ‘A single-index model with a surface-link for optimizing individualized dose rules’ (pre-print) for detail of the method. The main function of this package is simsl().