Visualizing and Analyzing Animal Track Data

Contains functions to access movement data stored in 'movebank.org' as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.

Functional Data Analysis for Density Functions by Transformation to a Hilbert Space

An implementation of the methodology described in Petersen and Mueller (2016) for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport.

Solve Generalized Estimating Equations for Clustered Data

Estimation of generalized linear models with correlated/clustered observations by use of generalized estimating equations (GEE). See e.g. Halekoh and Højsgaard, (2005, ), for details. Several types of clustering are supported, including exchangeable variance structures, AR1 structures, M-dependent, user-specified variance structures and more. The model fitting computations are performed using modified code from the 'geeM' package, while the interface and output objects have been written to resemble the 'geepack' package. The package also contains additional tools for working with and inspecting results from the 'geepack' package, e.g. a 'confint' method for 'geeglm' objects from 'geepack'.

A Collection of R Functions by the Petersen Lab

A collection of R functions that are widely used by the Petersen Lab. Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2024) and on CRAN: . The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025) , , .

Processes Calcium Imaging Data

Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. . < https://projecteuclid.org/euclid.aoas/1542078051>.

Estimate Time Varying Reproduction Numbers from Epidemic Curves

Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) and Wallinga and Teunis (2004) .

Discrete Distribution Approximations

Creates discretised versions of continuous distribution functions by mapping continuous values to an underlying discrete grid, based on a (uniform) frequency of discretisation, a valid discretisation point, and an integration range. For a review of discretisation methods, see Chakraborty (2015) .

Fits Piecewise Constant Models with Data-Adaptive Knots

Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.

Partial Separability and Functional Gaussian Graphical Models

Estimates a functional graphical model and a partially separable Karhunen-Loève decomposition for a multivariate Gaussian process. See Zapata J., Oh S. and Petersen A. (2019) .

Wasserstein Regression and Inference

Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) , including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) , including estimation, prediction, and inference of the Wasserstein autoregressive models.