Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint <doi:10.48550/arXiv.2105.03454>.

Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.

Parameter estimation and prediction of Gaussian Process Classifier models as described in Bachoc et al. (2020) <doi:10.1007/S10898-020-00920-0>. Important functions : gpcm(), predict.gpcm(), update.gpcm().

Provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (<https://www.researchsquare.com/article/rs-2363586/v1>). An interactive web application is also available at [GPEMR Web App](<https://gpem-r.shinyapps.io/GPEM-R/>).

Functionalities for modelling functional data with 
    multidimensional inputs, multivariate functional data, and non-separable 
    and/or non-stationary covariance structure of function-valued processes. 
    In addition, there are functionalities for functional regression models where 
    the mean function depends on scalar and/or functional covariates and 
    the covariance structure depends on functional covariates. 
    The development version of the package can be found on 
    <https://github.com/gpfda/GPFDA-dev>.