K-Means with Simultaneous Outlier Detection

An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.

Package for Reading Binary Files

Functions and analytics for GENEA-compatible accelerometer data into R objects. See topic 'GENEAread' for an introduction to the package. See < https://activinsights.com/technology/geneactiv/> for more details on the GENEActiv device.

Continuous Time SEM - 'OpenMx' Based Functions

Original 'ctsem' (continuous time structural equation modelling) functionality, based on the 'OpenMx' software, as described in Driver, Oud, Voelkle (2017) , with updated details in vignette. Combines stochastic differential equations representing latent processes with structural equation measurement models. These functions were split off from the main package of 'ctsem', as the main package uses the 'rstan' package as a backend now -- offering estimation options from max likelihood to Bayesian. There are nevertheless use cases for the wide format SEM style approach as offered here, particularly when there are no individual differences in observation timing and the number of individuals is large. For the main 'ctsem' package, see < https://cran.r-project.org/package=ctsem>.

Two Stage Hazard Rate Comparison

Two-stage procedure compares hazard rate functions, which may or may not cross each other.

k-Nearest Neighbor Join for Spatial Data

K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis.

Tools for Flexible Survival Analysis Using Machine Learning

Statistical tools for analyzing time-to-event data using machine learning. Implements survival stacking for conditional survival estimation, standardized survival function estimation for current status data, and methods for algorithm-agnostic variable importance. See Wolock CJ, Gilbert PB, Simon N, and Carone M (2024) .

Wrapper Functions Around 'Charles Schwab Individual Trader API'

For those wishing to interact with the 'Charles Schwab Individual Trader API' (< https://developer.schwab.com/products/trader-api--individual>) with R in a simplified manner, this package offers wrapper functions around authentication and the available API calls to streamline the process.

Inference and Prediction of Generic Physiologically-Based Kinetic Models

Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments. Moreover, it allows to account for any link between pairs of compartments, as well as any link of each of the compartments with the external medium. Such generic PBK models have today applications in pharmacology (PBPK models) to describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe chemical substance effects. In case of exposure to a parent compound (drug or chemical) the 'rPBK' package allows to consider metabolites, whatever their number and their phase (I, II, ...). Last but not least, package 'rPBK' can also be used for dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also Charles et al. (2022) .

Simulate from ODE-Based Models

Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.

Interface to 'Python' Package 'StepMix'

This is an interface for the 'Python' package 'StepMix'. It is a 'Python' package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. 'StepMix' handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings. Software paper available at .