The price action at any given time is determined by investor 
  sentiment and market conditions. Although there is no established principle, 
  over a long period of time, things often move with a certain periodicity.
  This is sometimes referred to as anomaly. 
  The seasonPlot() function in this package calculates and visualizes the 
  average value of price movements over a year for any given period. 
  In addition, the monthly increase or decrease in price movement is 
  represented with a colored background. 
  This seasonPlot() function can use the same symbols as the 'quantmod' package 
  (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).

A graphical user interface to the 'seasonal' package and
  'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. 
  Unifies the code base of <http://www.seasonal.website> and the GUI in the
  'seasonal' package.

An overall test for seasonality of a given time
    series in addition to a set of individual seasonality tests as
    described by Ollech and Webel (forthcoming): An overall seasonality
    test. Bundesbank Discussion Paper.

Provides tools for processing and evaluating seasonal weather forecasts, 
    with an emphasis on tercile forecasts. We follow the World Meteorological Organization's 
    "Guidance on Verification of Operational Seasonal Climate Forecasts", 
    S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL: <https://library.wmo.int/idurl/4/56227>). 
    The development was supported by the European Union’s Horizon 2020 research and innovation 
    programme under grant agreement no. 869730 (CONFER).
    A comprehensive online tutorial is available at <https://seasonalforecastingengine.github.io/SeaValDoc/>.

A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables. See Ryberg and York, 2020, <doi:10.3133/ofr20201082>.

Monte Carlo and MCMC sampling algorithms for semiparametric
    Bayesian regression analysis. These models feature a nonparametric
    (unknown) transformation of the data paired with widely-used
    regression models including linear regression, spline regression,
    quantile regression, and Gaussian processes. The transformation
    enables broader applicability of these key models, including for
    real-valued, positive, and compactly-supported data with challenging
    distributional features. The samplers prioritize computational
    scalability and, for most cases, Monte Carlo (not MCMC) sampling for
    greater efficiency. Details of the methods and algorithms are provided
    in Kowal and Wu (2023) <arXiv:2306.05498>.