Quantifies ecological memory in long time-series using Random Forest models ('Benito', 'Gil-Romera', and 'Birks' 2019 <doi:10.1111/ecog.04772>) fitted with 'ranger' (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>). Ecological memory is assessed by modeling a response variable as a function of lagged predictors, distinguishing endogenous memory (lagged response) from exogenous memory (lagged environmental drivers). Designed for palaeoecological datasets and simulated pollen curves from 'virtualPollen', but applicable to any long time-series with environmental drivers and a biotic response.

This project extends 'R' with a mechanism for efficient parallel data access by utilizing 'C++' shared memory. Large data objects can be accessed and manipulated directly from 'R' without redundant copying, providing both speed and memory efficiency. Memshare was published in Thrun, M.C., Märte J.: "Memshare: Memory Sharing for Multicore Computation in R with an Application to Feature Selection by Mutual Information using PDE" (2026), R Journal, <DOI:10.32614/RJ-2025-043>.

Data sets and sample analyses from Pinheiro and Bates,
  "Mixed-effects Models in S and S-PLUS" (Springer, 2000).

How much ram do you need to store a 100,000 by 100,000 matrix?
    How much ram is your current R session using? How much ram do you even have?
    Learn the scintillating answer to these and many more such questions with
    the 'memuse' package.

Encodes several methods for performing Mendelian randomization
    analyses with summarized data. Summarized data on genetic associations with the
    exposure and with the outcome can be obtained from large consortia. These data
    can be used for obtaining causal estimates using instrumental variable methods.

Provides an extensible framework for conducting simulations to
    compare data generating processes, item selection algorithms, parameter
    update algorithms, and stopping rules in computer adaptive testing (CAT)
    applications. Bundled algorithms include the Elo-based update rules of
    Klinkenberg, Straatemeier and van der Maas (2011)
    <doi:10.1016/j.compedu.2011.02.003> and Vermeiren, Kruis, Bolsinova, van der
    Maas and Hofman (2025) <doi:10.1016/j.caeai.2025.100376>.