Bayesian Clustering Using the Table Invitation Prior (TIP)

Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) . TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.

Charles's Utility Function using Formula

Utility functions that provides wrapper to descriptive base functions like cor, mean and table. It makes use of the formula interface to pass variables to functions. It also provides operators to concatenate (%+%), to repeat (%n%) and manage character vectors for nice display.

Clustering in the Discriminative Functional Subspace

The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Logic Regression

Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) . Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) .

Robust Mixture Discriminant Analysis

Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 , allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.

The FisherEM Algorithm to Simultaneously Cluster and Visualize High-Dimensional Data

The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) , is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided.

Model-Based Co-Clustering of Functional Data

The funLBM algorithm allows to simultaneously cluster the rows and the columns of a data matrix where each entry of the matrix is a function or a time series.

Download Data from the 'Office for National Statistics'

Provides functions to download and tidy statistical data published by the 'Office for National Statistics' < https://www.ons.gov.uk>. Covers GDP, inflation (CPI, CPIH, RPI), unemployment, employment, wages, trade, retail sales, house prices, productivity, population, and public sector finances. Most series are fetched from the 'ONS' website using its CSV time series endpoint. House price data is sourced from 'HM Land Registry' < https://www.gov.uk/government/organisations/land-registry>. Data is cached locally between sessions.

Download and Tidy 'HMRC' Statistical Data

Provides functions to download, parse, and tidy statistical data published by 'HM Revenue and Customs' ('HMRC') on 'GOV.UK'. Covers monthly tax receipts (41 tax heads from 2016), VAT (from 1973), fuel duties (from 1990), tobacco duties (from 1991), annual Corporation Tax receipts, stamp duty, research and development tax credit statistics (from 2000), tax gap estimates, Income Tax liabilities by income range, and monthly property transaction counts. File URLs are resolved at runtime via the 'GOV.UK' Content API < https://www.gov.uk/api/content>, so data is always current without hardcoded URLs. Files are cached locally between sessions.

Download Data from the 'Bank of England' Statistical Database

Provides functions to download and tidy statistical data published by the 'Bank of England' < https://www.bankofengland.co.uk>. Covers Bank Rate, 'SONIA', gilt yields, exchange rates, mortgage rates, mortgage approvals, consumer credit, and money supply. Series are fetched from the 'Bank of England Interactive Statistical Database' using its CSV endpoint. Data is cached locally between sessions.