Interactive Studio with Explanations for ML Predictive Models (modelStudio)
Automate explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of serverless HTML site. It combines ‘R’ with ‘D3.js’ to produce plots and descriptions for local and global explanations. The whole is greater than the sum of its parts, so it also supports EDA (Exploratory Data Analysis) on top of that. ‘modelStudio’ is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.

A Cross Between a 2D Density Plot and a Scatter Plot (ggpointdensity)
A cross between a 2D density plot and a scatter plot, implemented as a ‘ggplot2’ geom. Points in the scatter plot are colored by the number of neighboring points. This is useful to visualize the 2D-distribution of points in case of overplotting.

dplyr’ Extension for Common Panel Data Maneuvers (pmdplyr)
Using the ‘dplyr’ package as a base, adds a family of functions designed to make manipulating panel data easier. Allows the addition of indexing variables to a tibble to create a pibble, and the manipulation of data based on those indexing variables.

Hidden Markov Models for High Dimensional Data (hmmhdd)
Some algorithms for the study of Hidden Markov Models for two different types of data. For the study of univariate and multivariate data in a finite framework, we provide some methods based on the definition of a Gaussian copula function to define the dependence between data (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2018) <https://…/?id=776&tipo=add_qmox> ). For the study of functional data, we define an objective function based on distances between random curves to define the emission functions of the HMM (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2019) <https://…/?id=805&tipo=add_qmox> ).

Inference in Regressions with Shift-Share Structure (ShiftShareSE)
Provides confidence intervals in least-squares regressions when the variable of interest has a shift-share structure, and in instrumental variables regressions when the instrument has a shift-share structure. The confidence intervals implement the AKM and AKM0 methods developed in Adão, Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.