Parameter Estimation in Linear Transformation Model with
Interval-Censored Data and Covariate Measurement Error
Soutrik Mandal, Suojin Wang and Samiran Sinha
Estimates the parameters of the semiparametric linear transformation model using imputation method, naive method and regression calibration method when time-to-event is interval-censored and a covariate is measured with error. A right censoring indicator must be available. The methods implemented in this package can be found in Mandal, S., Wang, S. and Sinha, S. (2019+). Analysis of Linear Transformation Models with Covariate Measurement Error and Interval Censoring. (accepted, Statistics In Medicine).

Regression Models for Interval Censored Data
Regression models for interval censored data. Currently supports a semi-parametric Cox-PH and an imputation Cox-PH model which imputes the interval censored times based on Cox-PH model with an parametric baseline and then analyzes each imputed data set with the semi-parametric model.

Regression models for interval censored data. Currently supports a semi-parametric Cox-PH and proportional odds models (both fit via ic_sp) and fully parametric Cox-PH and proportional odds models (via ic_par). Easy diagnostics of fit can be evaluated via diag_covar and diag_baseline.

Regression models for interval censored data. Currently supports a semi-parametric Cox-PH and proportional odds models  and fully parametric Cox-PH and proportional odds models. Also includes functions for easy visual diagnostics of model fits.

Regression models for interval censored data. Currently supports Cox-PH and proportional odds models. Allows for both semi and fully parametric models. Includes functions for easy visual diagnostics of model fits.

Clifford Anderson-Bergman; the Eigen team for Eigen library included; uses Maarloes Maathius's HeightMap algorithm (MLEcens::reduc)
Regression models for interval censored data. Currently supports Cox-PH and proportional odds models. Allows for both semi and fully parametric models. Includes functions for easy visual diagnostics of model fits. Includes functions for fitting both the univariate and bivariate NPMLE.