Set of generalised tools for the flexible computation of climate 
  related indicators defined by the user. Each method represents a specific 
  mathematical approach which is combined with the possibility to select an 
  arbitrary time period to define the indicator. This enables a wide range of 
  possibilities to tailor the most suitable indicator for each particular climate 
  service application (agriculture, food security, energy, water management, health...). 
  This package is intended for sub-seasonal, seasonal and decadal climate 
  predictions, but its methods are also applicable to other time-scales, 
  provided the dimensional structure of the input is maintained. Additionally, 
  the outputs of the functions in this package are compatible with 'CSTools'. 
  This package is described in Pérez-Zanón et al. (2023) 
  <doi:10.1016/j.cliser.2023.100393> and it was developed in the context of 
  H2020 MED-GOLD (776467) and S2S4E (776787) projects. See Lledó et al. (2019) 
  <doi:10.1016/j.renene.2019.04.135> and Chou et al., 2023 
  <doi:10.1016/j.cliser.2023.100345> for details.

Provides datasets containing preformatted maps of Norway at
    the county, municipality, and ward (Oslo only) level for redistricting in 
    2024, 2020, 2018, and 2017. Multiple layouts are provided (normal, split, 
    and with an insert for Oslo), allowing the user to rapidly create choropleth
    maps of Norway without any geolibraries.

Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.

Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (Journal of the American Statistical Association 2025, <doi:10.1080/01621459.2024.2422124>).