Use the 'leaflet-timeline' plugin with a leaflet widget to add an
    interactive slider with play, pause, and step buttons to explore temporal
    geographic spatial data changes.

Instrumental variables (IVs) are a popular and powerful tool for 
  estimating causal effects in the presence of unobserved confounding. However, 
  classical methods rely on strong assumptions such as the exclusion criterion,
  which states that instrumental effects must be entirely mediated by 
  treatments. In the so-called "leaky" IV setting, candidate instruments are 
  allowed to have some direct influence on outcomes, rendering the average 
  treatment effect (ATE) unidentifiable. But with limits on the amount of 
  information leakage, we may still recover sharp bounds on the ATE, providing 
  partial identification. This package implements methods for ATE bounding in 
  the leaky IV setting with linear structural equations. For details, see Watson
  et al. (2024) <doi:10.48550/arXiv.2404.04446>.

Provides access to the 'Leanpub' API
    <https://leanpub.com/help/api> for gathering information about 
    publications and submissions to the 'Leanpub' platform.

An emulator designed for rapid sequential emulation (e.g., Markov chain Monte Carlo applications). Works via extension of the 'laGP' approach by Gramacy and Apley (2015 <doi:10.1080/10618600.2014.914442>). Details are given in Rumsey et al. (2023 <doi:10.1002/sta4.576>).

These functions take a gene expression value matrix, a
        primary covariate vector, an additional known covariates
        matrix.  A two stage analysis is applied to counter the effects
        of latent variables on the rankings of hypotheses.  The
        estimation and adjustment of latent effects are proposed by
        Sun, Zhang and Owen (2011).  "leapp" is developed in the
        context of microarray experiments, but may be used as a general
        tool for high throughput data sets where dependence may be
        involved.

Regression subset selection, including exhaustive search.