Interchange Tools for Multi-Parameter Spatiotemporal Data (mudata2)
Formatting and structuring multi-parameter spatiotemporal data is often a time-consuming task. This package offers functions and data structures designed to easily organize and visualize these data for applications in geology, paleolimnology, dendrochronology, and paleoclimate.

Spatial Downscaling using the Dissever Algorithm (dissever)
Spatial downscaling of coarse grid mapping to fine grid mapping using predictive covariates and a model fitted using the ‘caret’ package. The original dissever algorithm was published by Malone et al. (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al. (2017) <doi:10.1016/j.compag.2017.08.021>.

CARTOColors’ Palettes (rcartocolor)
Provides color schemes for maps and other graphics designed by ‘CARTO’ as described at <https://…/>. It includes four types of palettes: aggregation, diverging, qualitative, and quantitative.

Parsimonious Model-Based Clustering with Covariates (MoEClust)
Clustering via parsimonious Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2017) <arXiv:1711.05632>. This package fits finite Gaussian mixture models with gating and expert network covariates using parsimonious covariance parameterisations from ‘mclust’ via the EM algorithm. Visualisation of the results of such models using generalised pairs plots is also facilitated.

Distance Measures for Networks (NetworkDistance)
Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) <doi:10.3233/978-1-60750-692-8-227> for an overview on spectral class of inter-graph distance measures.

Simulation and Analysis Tools for Clinical Dose Response Modeling (clinDR)
Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references.

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