Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation
Damped Newton-Raphson Parameter Optimization
Lagrangian Multiplier Smoothing Splines: Mathematical Details
Compute Integrated Squared Second Derivative Penalty Matrix for Smooth...
Get Constrained GLM Coefficient Estimates
Get Centers for Partitioning
Generate Interaction Variable Patterns
Lagrangian Multiplier Smoothing Splines
Low-Level Fitting for Lagrangian Smoothing Splines
Calculate Matrix Inverse Square Root
Efficient Matrix Multiplication for $\textbf{A}^{T}\textbf{G}\textbf{A...
Finite-difference Gradient Computer
Matrix Inversion using Armadillo
Calculate Trace of Matrix Product $\text{trace}(\textbf{X}\textbf{U}\t...
Create Block Diagonal Matrix
Create One-Hot Encoded Matrix
Extract model coefficients
Collapse Matrix List into a Single Block-Diagonal Matrix
Compute Derivative of Penalty Matrix G with Respect to Lambda
Compute Derivative of w...
Compute Matrix Square Root Derivative
Tune Smoothing and Ridge Penalties via Generalized Cross Validation
Compute Derivative of Penalty Matrix G with Respect to Lambda (Wrapper...
Compute Eigenvalues and Related Matrices for G
Compute Component $\textbf{G}^{1/2}\textbf{A}(\textbf{A}^{T}\textbf{G}...
Compute Gram Matrix for Block Diagonal Structure
Construct Smoothing Spline Penalty Matrix
BFGS Implementation for REML Parameter Estimation
Efficient Matrix Multiplication
Generate Grid Indices Without expand.grid()
Find Extremum of Fitted Lagrangian Multiplier Smoothing Spline
Find Neighboring Cluster Partitions Using Midpoint Distance Criterion
Efficient Matrix Multiplication of G and A Matrices
Generate Posterior Samples from Fitted Lagrangian Multiplier Smoothing...
Wrapper for Smoothing Spline Penalty Computation
Generate Design Matrix with Polynomial and Interaction Terms
Efficiently Construct U Matrix
Compute Gram Matrix
Efficient Matrix Multiplication Operator
Matrix Inversion with Fallback Methods
Test if Vector is Binary
Expand Matrix into Partition Lists Based on Knot Boundaries
Compute Leave-One-Out Cross-Validated predictions for Gaussian Respons...
lgspline: Lagrangian Multiplier Smoothing Splines
Fit Lagrangian Multiplier Smoothing Splines
Compute Log-Likelihood for Weibull Accelerated Failure Time Model
Create Smoothing Spline Constraint Matrix
Compute First and Second Derivative Matrices
Create Data Partitions Using Clustering
Multiply Block Diagonal Matrices in Parallel
Left-Multiply a List of Block-Diagonal Matrices by U
Calculate Matrix Square Root
Compute Newton-Raphson Parameter Update with Numerical Stabilization
Plot Method for Lagrangian Multiplier Smoothing Spline Models
Predict Method for Fitted Lagrangian Multiplier Smoothing Spline
Print Method for lgspline Objects
Print Method for lgspline Object Summaries
Log-Prior Distribution Evaluation for lgspline Models
Compute softplus transform
Standardize Vector to Z-Scores
Summary method for lgspline Objects
Calculate Derivatives of Polynomial Terms
Calculate Second Derivatives of Interaction Terms
Unconstrained Generalized Linear Model Estimation
Unconstrained Weibull Accelerated Failure Time Model Estimation
Vector-Matrix Multiplication for Block Diagonal Matrices
Univariate Wald Tests and Confidence Intervals for Lagrangian Multipli...
Estimate Weibull Dispersion for Accelerated Failure Time Model
Weibull Family for Survival Model Specification
Weibull GLM Weight Function for Constructing Information Matrix
Compute gradient of log-likelihood of Weibull accelerated failure mode...
Estimate Scale for Weibull Accelerated Failure Time Model
Correction for the Variance-Covariance Matrix for Uncertainty in Scale
Implements Lagrangian multiplier smoothing splines for flexible nonparametric regression and function estimation. Provides tools for fitting, prediction, and inference using a constrained optimization approach to enforce smoothness. Supports generalized linear models, Weibull accelerated failure time (AFT) models, quadratic programming problems, and customizable arbitrary correlation structures. Options for fitting in parallel are provided. The method builds upon the framework described by Ezhov et al. (2018) <doi:10.1515/jag-2017-0029> using Lagrangian multipliers to fit cubic splines. For more information on correlation structure estimation, see Searle et al. (2009) <ISBN:978-0470009598>. For quadratic programming and constrained optimization in general, see Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. For a comprehensive background on smoothing splines, see Wahba (1990) <doi:10.1137/1.9781611970128> and Wood (2006) <ISBN:978-1584884743> "Generalized Additive Models: An Introduction with R".
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