Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data

Versatile method for ungrouping histograms (binned count data) assuming that counts are Poisson distributed and that the underlying sequence on a fine grid to be estimated is smooth. The method is based on the composite link model and estimation is achieved by maximizing a penalized likelihood. Smooth detailed sequences of counts and rates are so estimated from the binned counts. Ungrouping binned data can be desirable for many reasons: Bins can be too coarse to allow for accurate analysis; comparisons can be hindered when different grouping approaches are used in different histograms; and the last interval is often wide and open-ended and, thus, covers a lot of information in the tail area. Age-at-death distributions grouped in age classes and abridged life tables are examples of binned data. Because of modest assumptions, the approach is suitable for many demographic and epidemiological applications. For a detailed description of the method and applications see Rizzi et al. (2015) .

Discrete Global Grids

Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.

Cut Numeric Values into Evenly Distributed Groups

Package binr (pronounced as "binner") provides algorithms for cutting numerical values exhibiting a potentially highly skewed distribution into evenly distributed groups (bins). This functionality can be applied for binning discrete values, such as counts, as well as for discretization of continuous values, for example, during generation of features used in machine learning algorithms.

VPC Percentiles and Prediction Intervals

Perform a Visual Predictive Check (VPC), while accounting for stratification, censoring, and prediction correction. Using piping from 'magrittr', the intuitive syntax gives users a flexible and powerful method to generate VPCs using both traditional binning and a new binless approach Jamsen et al. (2018) with Additive Quantile Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) prediction correction.

Distance Sampling Detection Function and Abundance Estimation

A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) for more information on methods and < https://distancesampling.org/resources/vignettes.html> for example analyses.

Global Adaptive Generative Adjustment Algorithm for Generalized Linear Models

Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) . This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076).

Inky Color Schemes

Provides color palettes designed to be reminiscent of text on paper. The color schemes were taken from < https://stephango.com/flexoki>. Includes discrete, continuous, and binned scales that are not necessarily color-blind friendly. Simple scale and theme functions are available for use with 'ggplot2'.

Wearable Accelerometer Data File Readers

Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' < https://activinsights.com/>, .bin-format from GENEA devices (not for sale), and .cwa-format from 'Axivity' < https://axivity.com>. Further, it has functions for reading text files with epoch level aggregates from 'Actical', 'Fitbit', 'Actiwatch', 'ActiGraph', and 'PhilipsHealthBand'. Primarily designed to complement R package GGIR < https://CRAN.R-project.org/package=GGIR>.

Prepare and Explore Data for Palaeobiological Analyses

Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales < https://stratigraphy.org/chart/>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) .

Model-Agnostic Exposure-Response Plots

Provides a fluent mini-language for building exposure-response plots (model curves/ribbons, quantile-binned summaries, data strips, and grouped distribution panels) from observed data and a fitted exposure-response model. Designed to be model-agnostic: any model object that implements the er_predict() generic (and, optionally, er_simulate() and er_summary()) can be visualised.