mice - Multivariate Imputation by Chained Equations

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

Last updated 3 days ago

chained-equationsfcsimputationmicemissing-datamissing-valuesmultiple-imputationmultivariate-datacpp

16.50 score 462 stars 154 dependents 10k scripts 69k downloads

dscore - D-Score for Child Development

The D-score summarizes the child's performance on a set of milestones into a single number. The package implements four Rasch model keys to convert milestone scores into a D-score. It provides tools to calculate the D-score and its precision from the child's milestone scores, to convert the D-score into the Development-for-Age Z-score (DAZ) using age-conditional references, and to map milestone names into a generic 9-position item naming convention.

Last updated 7 months ago

child-developmentd-scoredazdevelopmental-trajectoriesgrowth-chartsrasch-modelcpp

6.89 score 8 stars 40 scripts 400 downloads

brokenstick - Broken Stick Model for Irregular Longitudinal Data

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.

Last updated 2 years ago

b-splinegrowth-curveslinear-mixed-modelslongitudinal-data

5.33 score 9 stars 12 scripts 402 downloads

AGD - Analysis of Growth Data

Tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands.

Last updated 10 months ago

anthropometrycdcdutchgrowthgrowth-chartslmswhoz-score

4.38 score 1 stars 48 scripts 455 downloads

childdevdata - Child Development Data

Measuring child development starts by collecting responses to developmental milestones, such as "able to sit" or "says two words". There are many ways to combine such responses into summaries. The package bundles publicly available datasets with individual milestone data for children aged 0-5 years, with the aim of supporting the construction, evaluation, validation and interpretation of methodologies that aggregate milestone data into informative measures of child development.

Last updated 2 years ago

child-developmentd-scoredataset

3.18 score 3 stars 8 scripts 193 downloads