# News from ESGtoolkit, ycinterextra, and nnetsauce

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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.

# 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.In

`MTS`

class (univariate and multivariate time series forecasting), no more lower and upper bounds

of prediction intervals. Only standard deviation is returned when`return_std`

is set to`True`

(see Python

example below for more details).Use of pandas DataFrames in Python, for

`MTS`

(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))

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