Adaptive Generalized PCA (adaptiveGPCA)
Implements adaptive gPCA, as described in: Fukuyama, J. (2017) <arXiv:1702.00501>. The package also includes functionality for applying the method to ‘phyloseq’ objects so that the method can be easily applied to microbiome data and a ‘shiny’ app for interactive visualization.

Fitting Mixed (Inflated and Adjusted) Distributions (gamlss.inf)
This is an add-on package to ‘gamlss’. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.

Power and Sample Size Analysis for One-way and Two-way ANOVA Models (pwr2)
User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.

Various Methods for Estimating Intrinsic Dimension (ider)
An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. Most methods implemented are based on different notion of fractal dimension such as the capacity dimension, the box-counting dimension, and the information dimension.

Functions for Base Types and Core R and ‘Tidyverse’ Features (rlang)
A toolbox for working with base types, core R features like the condition system, and core ‘Tidyverse’ features like tidy evaluation.