Unmatched Monotone Regression

Unmatched regression refers to the regression setting where covariates and predictors are collected separately/independently and so are not paired together, as in the usual regression setting. Balabdaoui, Doss, and Durot (2021) study the unmatched regression setting where the univariate regression function is known to be monotone. This package implements methods for computing the estimator developed in Balabdaoui, Doss, and Durot (2021). The main method is an active-set-trust-region-based method.

Downscaling Species Occupancy

Uses species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) .

Simulate from ODE-Based Models

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

Partial Orders and Relations

Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort).

Provides a Link Between the 'LSEG Datastream' System and R

Provides a set of functions and a class to connect, extract and upload information from the 'LSEG Datastream' database. This package uses the 'DSWS' API. Details of this API are available at < https://www.lseg.com/en/data-analytics>. Please report issues at < https://github.com/CharlesCara/DatastreamDSWS2R/issues>.

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) .

All Hierarchical or Graphical Generalized Linear Models

Find all hierarchical models of specified generalized linear model with information criterion (AIC, BIC, or AICc) within specified cutoff of minimum value. Alternatively, find all such graphical models. Use branch and bound algorithm so we do not have to fit all models.

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>.