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.

Continuous Time Structural Equation Modelling

Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See < https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. See < https://osf.io/preprints/psyarxiv/4q9ex_v2> for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see < https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see < https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . < https://cdriver.netlify.app/> contains some tutorial blog posts.

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.

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) .

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.

Clustering Communication Networks Using the Stochastic Topic Block Model Through Linkage.fr

It allows to cluster communication networks using the Stochastic Topic Block Model by posting jobs through the API of the linkage.fr server, which implements the clustering method. The package also allows to visualize the clustering results returned by the server.

Unified Climate Indices for Temperature, Precipitation, and Drought

Compute the standard suite of climate indices from daily weather observations. Provides the canonical 'ETCCDI' 27 (Expert Team on Climate Change Detection and Indices), the 'ET-SCI' heatwave and cold-wave families plus the Excess Heat Factor of Nairn and Fawcett (2013), and agroclimatic, drought, and human-comfort families. Drought indices ('SPI', 'SPEI') accept a choice of distribution (gamma or Pearson III for SPI; log-logistic or generalised extreme value for SPEI). Reference evapotranspiration is available via Hargreaves and the FAO-56 Penman-Monteith method (Allen et al. 1998). Percentile-based indices support the Zhang (2005) in-base bootstrap. Daily inputs are numeric vectors plus a 'Date' vector; outputs are tidy data frames. Optional gridded support via 'terra' applies any index over a 'SpatRaster' and reads 'netCDF' input. No external API calls; pairs with data packages such as 'readnoaa'. References: Alexander et al. (2006) ; Zhang et al. (2011) ; Zhang et al. (2005) .

Download and Tidy Australian Taxation Office Data

Fetch Australian Taxation Office (ATO) Taxation Statistics and related datasets via the data.gov.au Comprehensive Knowledge Archive Network ('CKAN') API < https://data.gov.au/data/api/3/>. Provides tidy access to individual, company, superannuation, goods and services tax (GST), fringe benefits tax (FBT), Voluntary Tax Transparency Code (VTTC), Pay As You Go (PAYG) withholding, charity, excise, and Corporate Tax Transparency data, plus Petroleum Resource Rent Tax, Medicare Levy Surcharge, and fuel tax credit aggregates. Includes reproducibility helpers (snapshot pinning, SHA-256 cache integrity, session manifest, optional 'Zenodo' deposit), classification crosswalks (ANZSIC 2006 to 2020, ANZSCO 2013 to 2021), panel harmonisation, reconciliation against Final Budget Outcome totals, and real-terms and per-capita helpers backed by bundled Australian Bureau of Statistics (ABS) Consumer Price Index and Estimated Resident Population series. Bridges to the 'taxstats' 2 per cent microdata sample via column-schema mapping. Data is published by the Australian Taxation Office under Creative Commons Attribution 2.5 Australia or 3.0 Australia licences (dataset-dependent).

Access Carbon Market Data from Emissions Trading Systems and Voluntary Registries

Unified access to carbon market data from compliance emissions trading systems ('EU ETS', 'UK ETS', 'RGGI', California Cap-and-Trade) and voluntary carbon markets (Verra, Gold Standard, American Carbon Registry, Climate Action Reserve, via the Berkeley Voluntary Registry Offsets Database and the 'CarbonPlan' 'OffsetsDB' API). Includes cross-market price data from the 'International Carbon Action Partnership' ('ICAP') Allowance Price Explorer < https://icapcarbonaction.com/en/ets-prices>, global carbon pricing from the World Bank Carbon Pricing Dashboard < https://carbonpricingdashboard.worldbank.org/>, and the historical 'RFF' World Carbon Pricing Database following Dolphin, Pollitt and Newbery (2020) . Data is downloaded from public sources on first use and cached locally.