2020-01-24 02:16:14 UTC; Eleanor Pullenayegum

2020-05-01 18:51:02 UTC; Eleanor Pullenayegum

2020-05-23 00:17:11 UTC; Eleanor Pullenayegum

2020-06-24 17:31:15 UTC; Eleanor Pullenayegum

2020-08-24 13:32:49 UTC; Eleanor Pullenayegum

Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).
2022-02-19 01:59:50 UTC; Eleanor Pullenayegum

Proper Scoring Rules for Missing Value Imputation
Loris Michel, Meta-Lina Spohn, Jeffrey Naef
Implementation of a KL-based scoring rule to assess the quality of different missing value imputations in the broad sense as introduced in Michel et al. (2021)  <arXiv:2106.03742>.

Fitting Ising models using the eLasso method
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary or continuous data.

Claudia van Borkulo, Sacha Epskamp, with contributions from Alexander Robitzsch
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.