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

Utility package containing: Main categories: Working with grouped data: 'do' something to data when  stratified 'by' some variables.  General linear estimates. Data handling utilities. Functional programming, in particular restrict functions to a smaller domain. Miscellaneous functions for data handling. Model stability in connection with model selection. Miscellaneous other tools.

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 <doi:10.48550/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 <doi:10.48550/arXiv.2008.09470>.