Implements high-dimensional multivariate regression by stacked generalisation (Rauschenberger 2021 <doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).
mice, earth, spls, MRCE, remMap, MultivariateRandomForest,
SiER, mcen, GPM, RMTL, MTPS

Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE from GitHub (<https://github.com/cran/MRCE>).

Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).

Penalized Likelihood Estimation under the Joint Cox Models
Between TTP and OS for Meta-Analysis
Perform regression analyses under the joint Cox proportional hazards model between TTP and OS for meta-analysis.
The method is applicable for meta-analysis combining several studies or for cluster survival data.

Penalized Likelihood Estimation under the Joint Cox Models
Between Tumour Progression and Death for Meta-Analysis
Perform regression analyses under the joint Cox proportional hazards model between tumour progression and death for meta-analysis.
The method is applicable for meta-analysis combining several studies or for cluster survival data.