Two-step modeling with separation of sources of variation through analysis of variance and subsequent multivariate
   modeling through a range of unsupervised and supervised statistical methods. Separation can focus on removal of interfering 
   effects or isolation of effects of interest. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. 
   (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.

Simulate and analyze multistate models with general hazard
    functions. gems provides functionality for the preparation of hazard functions
    and parameters, simulation from a general multistate model and predicting future
    events. The multistate model is not required to be a Markov model and may take
    the history of previous events into account. In the basic version, it allows
    to simulate from transition-specific hazard function, whose parameters are
    multivariable normally distributed.

Implements the GEMSS algorithm for sequential subdata selection 
    in large-scale Gaussian process modeling (Chang, Hua, and Wu, 2026) 
    <doi:10.1080/00401706.2026.2670596>. The method selects data points 
    by a criterion consisting of predictive and space-filling parts, enabling 
    efficient surrogate modeling for massive datasets.

Network meta-analyses (mixed treatment comparisons) in the Bayesian
    framework using JAGS. Includes methods to assess heterogeneity and
    inconsistency, and a number of standard visualizations.
    van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>;
    van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.

One can find single-stage and two-stage designs for a phase II 
    single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>.

Contains an engine for spatially-explicit eco-evolutionary mechanistic models with a modular implementation and several support functions. It allows exploring the consequences of ecological and macroevolutionary processes across realistic or theoretical spatio-temporal landscapes on biodiversity patterns as a general term. Reference: Oskar Hagen, Benjamin Flueck, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago F. Rangel, Loic Pellissier (2021) "gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity" <doi:10.1371/journal.pbio.3001340>.