Estimate the correlation between two NIfTI images across random parcellations of the images (Fortea et al., under review). This approach overcomes the problems of both voxel-based correlations (neighbor voxels may be spatially dependent) and atlas-based correlations (the correlation may depend on the atlas used).

Computes and plots the boundary between night and day.

Routines for enumerating all existing nonnegative integer solutions of a linear Diophantine equation. The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem.

Th-U-Pb electron microprobe age dating of monazite, as originally 
             described in <doi:10.1016/0009-2541(96)00024-1>.

Estimators and variance estimators tailored to the NILS hierarchical design (Adler et al. 2020, <https://res.slu.se/id/publ/105630>; Grafström et al. 2023, <https://res.slu.se/id/publ/128235>).
     The National Inventories of Landscapes in Sweden (NILS) is a long-term national monitoring program that collects, analyses and presents data on Swedish nature, covering both common and rare habitats <https://www.slu.se/om-slu/organisation/institutioner/skoglig-resurshushallning/miljoanalys/nils/>.

Miscellaneous R functions developed as collateral damage over the
    course of work in statistical and scientific computing for research. These
    include, for example, utilities that supplement existing idiosyncrasies of
    the R language, extend existing plotting functionality and aesthetics, help
    prepare data objects for imputation, and extend access to command line tools
    and systems-level information.

Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

A system for writing hierarchical statistical models largely
    compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models
    and do basic R-style math, and compiling both models and nimbleFunctions via
    custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace
    Approximation, Monte Carlo Expectation Maximization, and some other tools.
    The nimbleFunction system makes it easy to do things like implement new MCMC
    samplers from R, customize the assignment of samplers to different parts of
    a model from R, and compile the new samplers automatically via C++ alongside
    the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language
    by making it extensible: New distributions and functions can be added,
    including as calls to external compiled code. Although most people think
    of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models,
    one can use 'NIMBLE' for writing arbitrary other kinds of model-generic
    algorithms as well. A full User Manual is available at <https://r-nimble.org>.