Genetic Analysis Package

As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. ], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).

Convert Files that Use 'palmerpenguins' to Work with 'datasets'

From 'R' 4.5.0, the 'datasets' package includes the penguins and penguins_raw data sets popularised in the 'palmerpenguins' package. 'basepenguins' takes files that use the 'palmerpenguins' package and converts them to work with the versions from 'datasets' ('R' >= 4.5.0). It does this by removing calls to library(palmerpenguins) and making the necessary changes to column names. Additionally, it provides helper functions to define new files paths for saving the output and a directory of example files to experiment with.

Nested Dichotomy Logistic Regression Models

Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663.

Testing, Monitoring, and Dating Structural Changes: C++ Version

A fast implementation with additional experimental features for testing, monitoring and dating structural changes in (linear) regression models. 'strucchangeRcpp' features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g. cumulative/moving sum, recursive/moving estimates) and F statistics, respectively. These methods are described in Zeileis et al. (2002) . Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals, and their magnitude as well as the model fit can be evaluated using a variety of statistical measures.

Breaks for Additive Season and Trend

Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. 'BFAST' can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. 'BFAST' monitoring functionality is described in Verbesselt et al. (2010) . 'BFAST monitor' provides functionality to detect disturbance in near real-time based on 'BFAST'- type models, and is described in Verbesselt et al. (2012) . 'BFAST Lite' approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks).

Distribution of the 'BayesX' C++ Sources

'BayesX' performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the 'BayesX' command line tool for easy installation. A convenient R interface is provided in package R2BayesX.

Exact Variable-Subset Selection in Linear Regression

Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) .

Fast Wild Cluster Bootstrap Inference for Linear Models

Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, ) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.

Visualize Simple 2-D Decision Tree Partitions

Visualize the partitions of simple decision trees, involving one or two predictors, on the scale of the original data. Provides an intuitive alternative to traditional tree diagrams, by visualizing how a decision tree divides the predictor space in a simple 2D plot alongside the original data. The 'parttree' package supports both classification and regression trees from 'rpart' and 'partykit', as well as trees produced by popular frontend systems like 'tidymodels' and 'mlr3'. Visualization methods are provided for both base R graphics and 'ggplot2'.

Classification and Regression with Structured and Mixed-Type Data

Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon 'partykit' to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) .