Showing 9 of total 9 results (show query)
jbytecode
eive:An Algorithm for Reducing Errors-in-Variable Bias in Simple and Multiple Linear Regressions
Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.
Maintained by Mehmet Hakan Satman. Last updated 2 years ago.
compact-genetic-algorithmerrors-in-variableslinear-regressioncpp
44.6 match 1 stars 2.70 score 6 scriptsosorensen
hdme:High-Dimensional Regression with Measurement Error
Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
Maintained by Oystein Sorensen. Last updated 2 years ago.
11.0 match 8 stars 5.08 score 30 scriptsmaeveupton
reslr:Modelling Relative Sea Level Data
The Bayesian modelling of relative sea-level data using a comprehensive approach that incorporates various statistical models within a unifying framework. Details regarding each statistical models; linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, temporal splines (Upton et al 2023) <arXiv:2301.09556>, spatio-temporal splines (Upton et al 2023) <arXiv:2301.09556> and generalised additive models (Upton et al 2023) <arXiv:2301.09556>. This package facilitates data loading, model fitting and result summarisation. Notably, it accommodates the inherent measurement errors found in relative sea-level data across multiple dimensions, allowing for their inclusion in the statistical models.
Maintained by Maeve Upton. Last updated 1 years ago.
9.7 match 4 stars 5.23 score 28 scriptsr-forge
GPArotation:GPA Factor Rotation
Gradient Projection Algorithm Rotation for Factor Analysis. See '?GPArotation.Intro' for more details.
Maintained by Paul Gilbert. Last updated 2 months ago.
4.0 match 1 stars 12.66 score 1.1k scripts 362 dependentsjrlockwood
eivtools:Measurement Error Modeling Tools
This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression. It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.
Maintained by J.R. Lockwood. Last updated 3 years ago.
11.5 match 2.26 score 18 scriptsdavidsleonard
leiv:Bivariate Linear Errors-In-Variables Estimation
Estimate the slope and intercept of a bivariate linear relationship by calculating a posterior density that is invariant to interchange and scaling of the coordinates.
Maintained by David Leonard. Last updated 10 years ago.
9.6 match 2.00 score 3 scriptspasturm
bfsl:Best-Fit Straight Line
How to fit a straight line through a set of points with errors in both coordinates? The 'bfsl' package implements the York regression (York, 2004 <doi:10.1119/1.1632486>). It provides unbiased estimates of the intercept, slope and standard errors for the best-fit straight line to independent points with (possibly correlated) normally distributed errors in both x and y. Other commonly used errors-in-variables methods, such as orthogonal distance regression, geometric mean regression or Deming regression are special cases of the 'bfsl' solution.
Maintained by Patrick Sturm. Last updated 3 years ago.
1.5 match 3 stars 3.18 score 10 scriptsjohnlawrence1
SurvDisc:Discrete Time Survival and Longitudinal Data Analysis
Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.
Maintained by John Lawrence. Last updated 7 years ago.
1.3 match 1.00 score 6 scripts