Interpretable Machine Learning (iml)
Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>, partial dependence plots described by Friedman (2001) <http://…/2699986>, individual conditional expectation (‘ice’) plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of ‘lime’) described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x> and tree surrogate models.

Joint Modelling for Meta-Analytic (Multi-Study) Data (joineRmeta)
Fits joint models of the type proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extends to the multi-study, meta-analytic case. Functions for meta-analysis of a single longitudinal and a single time-to-event outcome from multiple studies using joint models. Options to produce plots for multi study joint data, to pool joint model fits from ‘JM’ and ‘joineR’ packages in a two stage meta-analysis, and to model multi-study joint data in a one stage meta-analysis.

Relative Growth Rate (petitr)
Calculates the relative growth rate (RGR) of a series of individuals by building a life table and solving the Lotka-Birch equation. (See Birch, L. C. 1948. The intrinsic rate of natural increase of an insect population. – Journal of Animal Ecology 17: 15-26) <doi:10.2307/1605>.

Boltzmann Machines with MM Algorithms (BoltzMM)
Provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. It follows the methods described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.

Joint Analysis and Imputation of Incomplete Data (JointAI)
Provides joint analysis and imputation of linear regression models, generalized linear regression models or linear mixed models with incomplete (covariate) data in the Bayesian framework. The package performs some preprocessing of the data and creates a ‘JAGS’ model, which will then automatically be passed to ‘JAGS’ <> with the help of the package ‘rjags’. It also provides summary and plotting functions for the output.