Weighted Cox-Regression for Nested Case-Control Data

Fit Cox proportional hazard models with a weighted partial likelihood. It handles one or multiple endpoints, additional matching and makes it possible to reuse controls for other endpoints Stoer NC and Samuelsen SO (2016) .

Tree-Structured Modelling of Varying Coefficients

Fitting tree-structured varying coefficient models (Berger et al. (2019), ). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.

Autocorrelation Regression Trees

A modified version of the classification and regression tree (CART) algorithm for modelling spatial data that features coordinate information. Coordinate information can be used to evaluate measures of spatial autocorrelation and spatial compactness during the splitting phase of the tree, leading to better predictions and more physically realistic predictions on these types of datasets. These methods are described in Ancell and Bean (2021) .

Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data

Estimation of functional linear mixed models for irregularly or sparsely sampled data based on functional principal component analysis.

Process-Based Biogeographical Analysis

Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors. Caetano et al (2020) .

Recursive Partitioning of Longitudinal Data

Performs recursive partitioning of linear and nonlinear mixed effects models, specifically for longitudinal data. The package is an extension of the original 'longRPart' package by Stewart and Abdolell (2013) < https://cran.r-project.org/package=longRPart>.

Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here ). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.

Estimate Structured Additive Regression Models with 'BayesX'

An R interface to estimate structured additive regression (STAR) models with 'BayesX'.

Random Network Model Estimation, Selection and Parameter Tuning

Model selection and parameter tuning procedures for a class of random network models. The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) , likelihood ratio method from Wang and Bickel (2015) , spectral methods from Le and Levina (2015) . Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 ) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 ). It also includes the consensus clustering of Gao et. al. (2014) , the method of moments estimation of nomination SBM of Li et. al. (2020) , and the network mixing method of Li and Le (2021) . It also includes the informative core-periphery data processing method of Miao and Li (2021) . The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Functional Linear Mixed Models for Densely Sampled Data

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.