GLDEX offers fitting algorithms corresponding to two major objectives. One is to provide a smoothing device to fit distributions to data using the weighted and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, and L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.

This package provides tools for creating detailed dataframes for common statistical approaches and tests. These include parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for ggstatsplot.

This package lets you fit beta regression and zero-or-one inflated beta regression and obtain Bayesian inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.

This package provides tools to identify and read BMP, JPEG, PNG, and TIFF format bitmap images. Identification defaults to the use of the magic number embedded in the file rather than the file extension.

This package provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.

This package provides some helpful extensions and modifications to the ggplot2 package to combine multiple ggplot2 plots into one and label them with letters, as is often required for scientific publications.

This package provides logicless templating, with a syntax that is not limited to R.

This package provides functions to impute using random forest. It operates under full conditional specifications (multivariate imputation by chained equations).

This tool provides an algorithm to identify rare cell types in single-cell data. It also identifies abundant cell types. The method is based on transcript counts obtained with unique molecular identifies.

This package provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. It also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors.

This R package provides functions to create formattable vectors and data frames. Formattable vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages.

This package implements two methods for performing a constrained principal component analysis (PCA), where non-negativity and/or sparsity constraints are enforced on the principal axes (PAs). The function nsprcomp computes one principal component (PC) after the other. Each PA is optimized such that the corresponding PC has maximum additional variance not explained by the previous components. In contrast, the function nscumcomp jointly computes all PCs such that the cumulative variance is maximal. Both functions have the same interface as the prcomp function from the stats package (plus some extra parameters).

With this package you can add in-app user authentication to Shiny, allowing you to secure publicly hosted apps and build dynamic user interfaces from user information.

This package adds distinctive yet unobtrusive geometric patterns where solid color fills are normally used. Patterned figures look just as professional when viewed by colorblind readers or when printed in black and white. The dozen included patterns can be customized in terms of scale, rotation, color, fill, line type, and line width. It is compatible with the ggplot2 package as well as grid graphics.

This package provides an interface to a large number of classification and regression techniques. These techniques include machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Also included:

• Generic resampling, including cross-validation, bootstrapping and subsampling;

• Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems;

• Filter and wrapper methods for feature selection;

• Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling.

Most operations can be parallelized.

This is a subset of the original spatstat package, containing the user-level code from spatstat which performs geometrical operations, except for the geometry of linear networks.

Circular Statistics, from "Topics in Circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

This package provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution with parameterization based on Vose (2008). The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.

This package contains functions for non-parametric survival analysis of exact and interval-censored observations.

This package provides support for iterators, which allow a programmer to traverse through all the elements of a vector, list, or other collection of data.

Hapassoc performs likelihood inference of trait associations with haplotypes and other covariates in generalized linear models (GLMs). The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs.

This package provides a differential evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that

1. do not sacrifice simplicity of design,

2. are essentially tuning-free, and

3. can be efficiently implemented directly in the R language.

This package provides cover-tree and kd-tree fast k-nearest neighbor search algorithms. Related applications including KNN classification, regression and information measures are implemented.

This package provides tools to interact with Google Sheets from within R.