Adds an LLM-powered chatbot to your 'shiny' app, that can
    turn your users' natural language questions into SQL queries that run
    against your data, and return the result as a reactive data frame. Use
    it to drive reactive calculations, visualizations, downloads, and
    more.

Translate 'SQL' 'SELECT' statements into lists of 'R' expressions.

Retrieve protein information from
    the 'UniProtKB' REST API (see <https://www.uniprot.org/help/api_queries>).

Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.

Set of functions to make the processing and analysis of
    surveys easier : interactive shiny apps and addins for data recoding,
    contingency tables, dataset metadata handling, and several convenience
    functions.

A method for prediction of environmental conditions based on 
    transcriptome data linked with the environmental gradients. This package 
    provides functions to overview gene-environment relationships, to construct 
    the prediction model, and to predict environmental conditions where the 
    transcriptomes were generated. This package can quest for candidate genes 
    for the model construction even in non-model organisms' transcriptomes 
    without any genetic information.

Implementation of a computationally efficient method for
    simulating queues with arbitrary arrival and service times. 
    Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) 
    for further details.