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Miscellaneous Functions for Working with 'stars' Rasters
Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.
Circular Analyses Helper Functions
Light-weight functions for computing descriptive statistics in different circular spaces (e.g., 2pi, 180, or 360 degrees), to handle angle-dependent biases, pad circular data, and more. Specifically aimed for psychologists and neuroscientists analyzing circular data. Basic methods are based on Jammalamadaka and SenGupta (2001)
Autocorrelation Regression Trees
A modified version of the classification and regression tree (CART)
algorithm for modelling spatial data that features coordinate information.
Coordinate information can be used to evaluate measures of spatial
autocorrelation and spatial compactness during the splitting phase of the
tree, leading to better predictions and more physically realistic predictions
on these types of datasets. These methods are described in Ancell and Bean (2021)
Weighted Cox-Regression for Nested Case-Control Data
Fit Cox proportional hazard models with a weighted
partial likelihood. It handles one or multiple endpoints, additional matching
and makes it possible to reuse controls for other endpoints
Stoer NC and Samuelsen SO (2016)
Regression Methods for Interval-Valued Variables
Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.
Extrema-Weighted Feature Extraction
Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in van den Boom et al. (2018)
Single-Index Models with Multiple-Links
A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020)
Causal Generalized Linear Models
An implementation of methods for causal discovery in a structural causal model where the conditional distribution of the target node is described by a generalized linear model conditional on its causal parents.
Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data
Estimation of functional linear mixed models for irregularly or sparsely sampled data based on functional principal component analysis.
Process-Based Biogeographical Analysis
Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors. Caetano et al (2020)