Changes in version 0.37.0                        

Date: 2026-05-08

  - Add condvolf

                       Changes in version 0.36.0                        

  - Add sensitivity analysis to ahead::dynrmf

                       Changes in version 0.32.0                        

  - Add contextridge2f for adding context to ridge2f forecasting.
  - Add stackeridge2f for time series stacked generalization

                       Changes in version 0.25.0                        

  - Implement ANY MODEL+GARCH(1, 1) forecasting for stocks (stochastic
    volatility models)
  - armagarchf doesn't use the bootstrap by default anymore

                       Changes in version 0.24.0                        

  - add fitted values in ridge2f

                       Changes in version 0.22.0                        

  - add explicit clustering to volatility forecasting in mlarch

                       Changes in version 0.18.0                        

  - Add ML-ARCH model, see vignettes for more examples

                       Changes in version 0.17.0                        

  - Add conformal prediction to ridge2f (with KDE, bootstrap, block
    bootstrap and sequential split conformal prediction)
  - Faster install, less imports

                       Changes in version 0.14.0                        

  - Add fit_func and predict_func for custom fitting and prediction
    functions of ahead::dynrmf (using caret Machine Learning).
  - Add forecasting combinations based on ForecastComb, adding Ridge and
    Elastic Net to the mix.

                       Changes in version 0.11.0                        

  - Include tests (90% coverage). After cloning, run:

install.packages("covr")
covr::report()

                       Changes in version 0.10.0                        

  - Univariate forecasting for ridge2f. See
    https://thierrymoudiki.github.io/blog/2024/02/26/python/r/julia/ahead-v0100.
  - Fast calibration for ridge2f (univariate and multivariate case). See
    https://thierrymoudiki.github.io/blog/2024/02/26/python/r/julia/ahead-v0100.

                        Changes in version 0.9.0                        

  - progress bars for bootstrap (independent, circular block, moving
    block)

                        Changes in version 0.8.0                        

  - empirical marginals for R-Vine copula simulation
  - risk-neutralize simulations

                        Changes in version 0.7.0                        

  - moving block bootstrap in ridge2f, basicf and loessf, in addition to
    circular block bootstrap from 0.6.2
  - adjust R-Vine copulas on residuals for ridge2f simulation
  - new plots for simulations see (new) vignettes
  - split conformal prediction intervals (very very experimental and
    basic right now, too conservative)
  - Depends and selective Imports (beneficial to Python and rpy2 for
    installation time?)
  - getsimulations extracts simulations from a given time series (from
    ridge2f and basicf)
  - getreturns extracts returns/log-returns from multivariate time
    series
  - splitts splits time series using a proportion of data

                        Changes in version 0.6.2                        

  - Add Block Bootstrap to ridge2f
  - Add external regressors to ridge2f
  - Add clustering to ridge2f
  - Add Block Bootstrap to loessf
  - Create new vignettes for ridge2f and loessf

                        Changes in version 0.6.1                        

  - Align version with Python's
  - Temporarily remove dependency with cclust

                        Changes in version 0.6.0                        

  - Include basic methods: mean forecast, median forecast, random walk
    forecast

                        Changes in version 0.5.0                        

  - add dropout regularization to ridge2f
  - parallel execution for type_pi == bootstrap in ridge2f (done in R
    /!, experimental)
  - preallocate matrices for type_forecast == recursive in ridge2f

                        Changes in version 0.4.2                        

  - new attributes mean, lower bound, upper bound forecast as numpy
    arrays

                        Changes in version 0.4.1                        

  - use get_frequency to get series frequency as a number
  - create a function get_tscv_indices for getting time series
    cross-validation indices