Meta-Analysis of Phase I Dose-Finding Early Clinical Trials (dfmeta)
Meta-analysis approaches for Phase I dose finding early phases clinical trials in order to better suit requirements in terms of maximum tolerated dose (MTD) and maximal dose regimen (MDR). This package has currently three different approaches: (a) an approach proposed by Zohar et al, 2011, <doi:10.1002/sim.4121> (denoted as ZKO), (b) the Variance Weighted pooling analysis (called VarWT) and (c) the Random Effects Model Based (REMB) algorithm, where user can input his/her own model based approach or use the existing random effect logistic regression model (named as glimem) through the ‘dfmeta’ package.

Nonlinear Regression Modelling using Robust Methods (nlr)
Non-Linear Robust package is developed to handle the problem of outliers in nonlinear regression, using robust statistics. It covers classic methods in nonlinear regression as well. It has facilities to fit models in the case of auto correlated and heterogeneous variance cases, while it include tools to detecting outliers in nonlinear regression. (Riazoshams H, Midi H, and Ghilagaber G, (2018, ISBN:978-1-118-73806-1). Robust Nonlinear Regression, with Application using R, Joh Wiley and Sons.)

Latent Space Models for Multidimensional Networks (spaceNet)
Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166>.

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