GAMs, GAMMs and other generalized ridge regression with multiple smoothing parameter estimation by GCV, REML or UBRE/AIC. The library includes a gam() function, a wide variety of smoothers, JAGS support and distributions beyond the exponential family.

Automated performance of common transformations used to fulfill parametric assumptions of normality and identification of the best performing method for the user. Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. <DOI: 10.1201/9780203910894>. <DOI: 10.22237/jmasm/1257034080>.

Assists in statistical model building to find optimal and semi-optimal higher order interactions and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution algorithm comes up with model forms of a specific type that can have fixed variables, higher order interactions and their lower order terms.

rocHPL is a benchmark based on the HPL benchmark application, implemented on top of AMD's Radeon Open Compute ROCm Platform, runtime, and toolchains. rocHPL is created using the HIP programming language and optimized for AMD's latest discrete GPUs.

It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.

This package provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM). See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Datasets to Accompany S. Weisberg (2014, ISBN: 978-1-118-38608-8), "Applied Linear Regression," 4th edition. Many data files in this package are included in the `alr3` package as well, so only one of them should be used.

Calculates pointwise confidence intervals for the cumulative distribution function of the event time for current status data, data where each individual is assessed at one time to see if they had the event or not by the assessment time.

Package for the analysis of categorical functional data. The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>. It also provides functions to perform basic statistical analysis on categorical functional data.

This package implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.

This package implements the Determinantal point process (DPP) based optimal design emulator described in Pratola, Lin and Craigmile (2018) <arXiv:1804.02089> for Gaussian process regression models. See <http://www.matthewpratola.com/software> for more information and examples.

Use leaf physiognomic methods to reconstruct mean annual temperature (MAT), mean annual precipitation (MAP), and leaf dry mass per area (Ma), along with other useful quantitative leaf traits. Methods in this package described in Lowe et al. (in review).

This package contains functions for the DivE estimator <doi:10.1371/journal.pcbi.1003646>. The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.

This package provides a set of tools for empirical analysis of diversity (a number and frequency of different types in a population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.

Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.

Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.

Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories. <arXiv:1305.0717>.

Reads the output of the PerkinElmer InForm software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.

Fit joint mean-covariance models for longitudinal data. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Armadillo C++ library for numerical linear algebra and RcppArmadillo glue.

Matrix-Based Flexible Project Planning. This package models, plans, and schedules flexible, such as agile, extreme, and hybrid project plans. The package contains project planning, scheduling, and risk assessment functions. Kosztyan (2022) <doi:10.1016/j.softx.2022.100973>.

This package provides methods for controlling the median of the false discovery proportion (mFDP). Depending on the method, simultaneous or non-simultaneous inference is provided. The methods take a vector of p-values or test statistics as input.

Multi-omic (or any multi-view) spectral clustering methods often assume the same number of clusters across all datasets. We supply methods for multi-omic spectral clustering when the number of distinct clusters differs among the omics profiles (views).

Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and best k treatments POTH can also be calculated to improve interpretation of treatment hierarchies.

Implement the algorithm provided in scan for estimating the transmission route on railway network using passenger volume. It is a generalization of the scan statistic approach for railway network to identify the hot railway route for transmitting infectious diseases.