Non-Linear Mixed-Effects Modelling using Stochastic Differential Equations (PSM)
Functions for fitting linear and non-linear mixed-effects models using stochastic differential equations (SDEs). The provided pipeline relies on the coupling of the FOCE algorithm and Kalman filtering as outlined by Klim et al (2009) <doi:10.1016/j.cmpb.2009.02.001> and has been validated against the proprietary software NONMEM (Tornøe et al, 2005, <doi:10.1007/s11095-005-5269-5>). Further functions are provided for finding smoothed estimates of model states and for simulation. The package allows for any multivariate non-linear time-variant model to be specified, and it also handles multidimensional input, covariates, missing observations, and specification of dosage regimen.

A Structural Equation Embedded Likelihood Framework for Causal Discovery (SELF)
Provides the SELF criteria to learn causal structure. Details of the algorithm can be found in ‘SELF: A Structural Equation Embedded Likelihood Framework for Causal Discovery’ (AAAI 2018).

Covariate Adaptive Clustering (predkmeans)
Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.

Store Data About Rows (keyholder)
Tools for keeping track of information, named ‘keys’, about rows of data frame like objects. This is done by creating special attribute ‘keys’ which is updated after every change in rows (subsetting, ordering, etc.). This package is designed to work tightly with ‘dplyr’ package.

Conservation Planning Data Sets (prioritizrdata)
Conservation planning data sets and tutorials for learning how to use the ‘prioritizr’ package <https://…/package=prioritizr>.

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