In this post, I introduce new versions of ESGtoolkit, ycinterextra, and nnetsauce.

  • ESGtoolkit (for R) is a toolkit for Monte Carlo Simulation in Finance, Economics, Insurance, Physics, etc.
  • ycinterextra (for R) is used for yield curve interpolation and extrapolation
  • nnetsauce (for Python and R) does supervised Statistical/Machine Learning using Randomized and Quasi-Randomized neural networks

Contents

Feel free to jump directly to the section that has your interest:

1-ESGtoolkit

ESGtoolkit is no longer available from CRAN (archived). It can be installed from GitHub or from R universe.

installing ESGtoolkit

  • From Github:
library(devtools)
devtools::install_github("Techtonique/ESGtoolkit")
  • From R universe:
# Enable universe(s) by techtonique
options(repos = c(
  techtonique = 'https://techtonique.r-universe.dev',
  CRAN = 'https://cloud.r-project.org'))

# Install some packages
install.packages('ESGtoolkit')

news from ESGtoolkit

In version v0.4.0, spline interpolation (stats::spline) is used for forward rates’ computation in esgfwdrates. New (and only, so far) interpolation options are: “fmm”, “periodic”, “natural”, “hyman” (type ?stats::spline in R console for more details on each interpolation method).

Here is an example (in function simG2plus, and more specifically for methodyc, whose possible values are now “fmm”, “periodic”, “natural”, “hyman”) in which you can see how this new choices will affect the simulation results.

G2++ simulations

2-ycinterextra

ycinterextra is no longer available from CRAN (archived). It can be installed from GitHub or from R universe.

installing ycinterextra

  • From Github:
devtools::install_github("Techtonique/ycinterextra")
  • From R universe:
# Enable universe(s) by techtonique
options(repos = c(
  techtonique = 'https://techtonique.r-universe.dev',
  CRAN = 'https://cloud.r-project.org'))

# Install some packages
install.packages('ycinterextra')

news from ycinterextra

In version 0.2.0

  • Rename function as.list to a tolist doing the same thing. ycinterextra::as.list was notably causing bugs in Shiny applications
  • Refactor the code, to make it more readable

3-nnetsauce

installing nnetsauce

Python version:

  • 1st method: by using pip at the command line for the stable version
pip install nnetsauce
  • 2nd method: using conda (Linux and macOS only for now)
conda install -c conda-forge nnetsauce
  • 3rd method: from Github, for the development version
pip install git+https://github.com/Techtonique/nnetsauce.git

R version:

library(devtools)
devtools::install_github("Techtonique/nnetsauce/R-package")
library(nnetsauce)

news from nnetsauce

In version 0.11.3:

  • Implementation of a RandomBagRegressor; an ensemble of CustomRegressors in which diversity is achieved by sampling the columns and rows of an input dataset. A Python example can be found here on GitHub.

  • Use of pandas DataFrames in Python, for MTS (a work in progress!) (see Python example below for details)

# Using a data frame input for forecasting with `MTS`

import nnetsauce as ns
import pandas as pd
from sklearn import linear_model


dataset = {
'date' : ['2001-01-01', '2002-01-01', '2003-01-01', '2004-01-01', '2005-01-01'],
'series1' : [34, 30, 35.6, 33.3, 38.1],    
'series2' : [4, 5.5, 5.6, 6.3, 5.1],
'series3' : [100, 100.5, 100.6, 100.2, 100.1]}
df = pd.DataFrame(dataset).set_index('date')
print(df)

# Adjust Bayesian Ridge and predict
regr5 = linear_model.BayesianRidge()
obj_MTS = ns.MTS(regr5, lags = 1, n_hidden_features=5)
obj_MTS.fit(df)
print(obj_MTS.predict())

# with credible intervals
print(obj_MTS.predict(return_std=True, level=80))
print(obj_MTS.predict(return_std=True, level=95))