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.

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.

Meta Analysis Instrumental Variable Estimator

Meta-analysis traditionally assigns more weight to studies with lower standard errors, assuming higher precision. However, in observational research, precision must be estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical significance. This can lead to spurious precision, invalidating inverse-variance weighting and bias-correction methods like funnel plots. Common methods for addressing publication bias, including selection models, often fail or exacerbate the problem. This package introduces an instrumental variable approach to limit bias caused by spurious precision in meta-analysis. Methods are described in 'Irsova et al.' (2025) .

Continuous Time Structural Equation Modelling - Old 'OpenMx'-Based Version

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. This package is maintained for consistency with the original 'ctsem' paper, but for the much newer and more capable 'ctsem' package, see < https://cran.r-project.org/package=ctsem>.

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.

Advanced 'tryCatch()' and 'try()' Functions

Advanced tryCatch() and try() functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).

Continuous Time Structural Equation Modelling

Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See < https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. See < https://osf.io/preprints/psyarxiv/4q9ex_v2> for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see < https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see < https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . < https://cdriver.netlify.app/> contains some tutorial blog posts.

Two Stage Hazard Rate Comparison

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

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) .

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.