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Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression
Functions are provided to calculate and display ridge TRACE Diagnostics for a
variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood
estimation of unknown true effects under normal distribution-theory, some estimates are
modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality
of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE
Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.)
The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain
(2022)
Modeling Animal Movement with Continuous-Time Discrete-Space Markov Chains
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015)
A LazyData Facility
Supplies a LazyData facility for packages which have data sets but do not provide LazyData: true. A single function is is included, requireData, which is a drop-in replacement for base::require, but carrying the additional functionality. By default, it suppresses package startup messages as well. See argument 'reallyQuitely'.
Doubly Robust Distribution Balancing Weighting Estimation
Implements the doubly robust distribution balancing weighting proposed by Katsumata (2024)
A Multi-Process 'dplyr' Backend
Partition a data frame across multiple worker processes to provide simple multicore parallelism.
Data Analysis and Graphics Data and Functions
Functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.
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.
Fast Covariance Estimation for Sparse Functional Data
We implement the Fast Covariance Estimation for
Sparse Functional Data paper published in Statistics and Computing
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)
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.