Flexibly convert data between long and wide format using just two
    functions: reshape_toLong() and reshape_toWide().

A dimension reduction technique for outlier detection. DOBIN: a Distance 
    based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier 
    detection. This is not an outlier detection method; rather it is a pre-processing 
    method for outlier detection. It brings outliers to the fore-front using fewer basis 
    vectors (Kandanaarachchi, Hyndman 2020) <doi:10.1080/10618600.2020.1807353>.

Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.

Utility package containing:
    1) Facilities for working with grouped data: 'do' something to data
    stratified 'by' some variables.
    2) LSmeans (least-squares means), general linear estimates.
    3) Restrict functions to a smaller domain.
    4) Miscellaneous other utilities.

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.

Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms,
    namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. 
    The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <arXiv:1405.4053>.
    The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. 
    Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering.
    It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps
    these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm 
    and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense
    areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the 
    document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can 
    be found which are representative of the topic.
    More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at <arXiv:2008.09470>.