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Bayesian Linear Mixed-Effects Models
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013)
Read and Process 'Pamguard' Binary Data
Functions for easily reading and processing binary data files created by 'Pamguard' (< https://www.pamguard.org/>). All functions for directly reading the binary data files are based on 'MATLAB' code written by Michael Oswald.
Computation and Plots of Influence Functions for Risk and Performance Measures
Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) < https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) < https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.
Multi-Resolution Kriging Based on Markov Random Fields
Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research.
Vault Client for Secrets and Sensitive Data
Provides an interface to a 'HashiCorp' vault server over its http API (typically these are self-hosted; see < https://www.vaultproject.io>). This allows for secure storage and retrieval of secrets over a network, such as tokens, passwords and certificates. Authentication with vault is supported through several backends including user name/password and authentication via 'GitHub'.
Nonlinear least squares examples from NIST
Datasets for testing nonlinear regression routines.
Higher Order Inference for Nonlinear Heteroscedastic Models
Implements likelihood inference based on higher order approximations for nonlinear models with possibly non constant variance.
Nonlinear Regression for Agricultural Applications
Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003)
A Distributed Worker Launcher Framework
In computationally demanding analysis projects,
statisticians and data scientists asynchronously
deploy long-running tasks to distributed systems,
ranging from traditional clusters to cloud services.
The 'NNG'-powered 'mirai' R package by Gao (2023)
Sample Size for SMART Designs in Non-Surgical Periodontal Trials
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+)