* Searchable Variable Explorer with Labelled Variables* (

**varsExplore**)

Creates a summary dataframe that can be used in ‘RStudio’ similar to the variable explorer in ‘Stata’, but which also includes the summary statistics. By default the result is shown in the ‘RStudio’ Viewer Pane as a searchable data table. This is useful particularly if you have a large dataset with a very large number of labelled variables with hard to remember names. Can also be used to generate a table of summary statistics.

*(*

**Goodness-of-Fit Tests for Functional Data****goffda**)

Implementation of several goodness-of-fit tests for functional data. Currently, mostly related with the functional linear model with functional/scalar response and functional/scalar predictor. The package allows for the replication of the data applications considered in García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga (2019) <arXiv:1909.07686>.

*(*

**Density, Probability, Quantile (‘DPQ’) Computations****DPQ**)

Computations for approximations and alternatives for the ‘DPQ’ (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. — This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve R`s own pbeta(), qgamma(), …, etc: {”dpq”-functions}. — We plan to complement with ‘DPQmpfr’ to be suggested later.

*(*

**Multiple Testing of Local Extrema for Detection of Change Points****mSTEM**)

A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.

*(*

**Design and Analysis for Factorial Experiments****factorEx**)

Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2019+), and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.