Studies that report shifts in species distributions may be biased
  by the shape of the study area. The main functionality of this package is to
  calculate the Latitudinal Bias Index (LBI) for any given shape. The LBI is
  bounded between +1 (100% probability to exclusively record latitudinal
  shifts, i.e., range shifts data sampled along a perfectly South-North
  oriented straight line) and -1 (100% probability to exclusively record
  longitudinal shifts, i.e., range shifts data sampled along a perfectly
  East-West oriented straight line).

Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) <doi:10.31234/osf.io/dy79z>.

A graph
  proposed by Rosenbaum is useful
  for checking some properties of various
  sorts of latent scale, this program generates commands
  to obtain the graph using 'dot' from 'graphviz'.

Estimating causal parameters in the presence of treatment spillover is of great interest in statistics.
    This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form.
    The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect.
    The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.

Effectively simulates the discretization process inherent to
    Likert scales while minimizing distortion.  It converts continuous
    latent variables into ordinal categories to generate Likert scale item
    responses.  Particularly useful for accurately modeling and analyzing
    survey data that use Likert scales, especially when applying
    statistical techniques that require metric data.

Bayesian model averaging (BMA) algorithms for univariate link latent Gaussian models (ULLGMs). For detailed information, refer to Steel M.F.J. & Zens G. (2024) "Model Uncertainty in Latent Gaussian Models with Univariate Link Function" <doi:10.48550/arXiv.2406.17318>. The package supports various g-priors and a beta-binomial prior on the model space. It also includes auxiliary functions for visualizing and tabulating BMA results. Currently, it offers an out-of-the-box solution for model averaging of Poisson log-normal (PLN) and binomial logistic-normal (BiL) models. The codebase is designed to be easily extendable to other likelihoods, priors, and link functions.