rACMEMEEV1.0.1 package

Multi-Variate Measurement Error Adjustment

acf_plots

Create Auto Correlation Plots for Models

acme_model

Model the Data

attenuation_matrix

Create Attenuation Contamination Matrix

create_model_string

Define a Model that is JAGS Usable

create_modelling_data

Data Preparation and Formatting for Modelling

create_stan_model_string

Define the Pre-Model Using Stan

fisher_z_transform

Perform the Fisher Z Transformation

generate_coefficient

Generate updated validity coefficient using Fisher Z Transformation

multivariate_model

Model the Data with Multivariate Adjustment

pipeline

Pipeline used for running a model start to finish.

plot_a_list

Custom Function to Use Patchwork

plot_covariates

Diagnostics for Models

sensitivity_analysis

Run a sensitivity analysis on the error adjustment

standardize_with_return

Standardize the data of 1-D vector

traceplots

Create Traceplots for the Parameters

Download source packageRead PDF manual

A methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.

  • Maintainer: Alexander Lee
  • License: MIT + file LICENSE
  • Last published: 2025-12-13

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