Subdistribution Analysis of Competing Risks

Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 , and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, .

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.

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) , , .

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'.

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>.

Interpolation and Extrapolation for Three Dimensions of Biodiversity

Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. 'iNEXT.3D' extends 'iNEXT' to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) for more details.

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) .