Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2020b) <\doi{10.1007/s11222-020-09945-7}>, Augugliaro et al. (2020a) <\doi{10.1093/biostatistics/kxy043}>, Yin et al. (2001) <\doi{10.1214/11-AOAS494}> and Stadler et al. (2012) <\doi{10.1007/s11222-010-9219-7}>.

Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2020b) <doi: 10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi: 10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi: 10.1214/11-AOAS494> and Stadler et al. (2012) <doi: 10.1007/s11222-010-9219-7>.

Fits Conditional Generalized Linear Models
Estimates the ratio of the regression coefficients and the dispersion parameter in conditional generalized linear models for clustered data.
Rcpp (>= 0.12.18), stats, nleqslv, data.table

Clean and Analyze Continuous Glucose Monitor Data
person("Tim", "Vigers", email = "tim.b.vigers@gmail.com", role = c("aut", "cre"))
This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom' (<https://www.dexcom.com>) or 'iPro 2' (<http://professional.medtronicdiabetes.com/ipro2-professional-cgm>) data. The makecsv() function can convert files to CSV if they have been saved as 'Excel' documents. The cleaniprodata() and cleandexcomdata() functions take a directory of CGM data files and clean them for analysis. Finally, cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file. This spreadsheet is compatible with REDCap data upload ("--1" is added to each subject ID automatically for double data entry).