Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).

The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.

Helpful functions for the cleaning and manipulation of surveillance data, especially with regards to the creation and validation of panel data from individual level surveillance data.

Perform bulk and cell type-specific expression quantitative trail loci mapping with our novel method (Little et al. (2023) <doi:10.1038/s41467-023-38795-w>).

This package provides functions for evaluating and visualizing ecological assessment procedures for surface waters containing physical, chemical and biological assessments in the form of value functions.

Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.

This package provides functions for financial analysis and financial modeling, including batch graphs generation, beta calculation, descriptive statistics, annuity calculation, bond pricing and financial data download.

This package provides a Gaussian or Student's t copula-based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.

Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals.

This package provides methods for fitting log-link GLMs and GAMs to binomial data, including EM-type algorithms with more stable convergence properties than standard methods.

Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks.

This package provides methods of selecting one from many numeric predictors for a regression model, to ensure that the additional predictor has the maximum effect size.

This package provides functions are primarily functions for systems of ordinary differential equations, difference equations, and eigenanalysis and projection of demographic matrices; data are for examples.

Features unstructured, structured and reverse geocoding using the photon geocoding API <https://photon.komoot.io/>. Facilitates the setup of local photon instances to enable offline geocoding.

Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.

Develops a framework for fisheries stock assessment simulation testing with Stock Synthesis (SS) as described in Anderson et al. (2014) <doi:10.1371/journal.pone.0092725>.

Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.

Converts the floor speeches of Uruguayan legislators, extracted from the parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator.

Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.

Create HTML tables of descriptive statistics, as one would expect to see as the first table (i.e. "Table 1") in a medical/epidemiological journal article.

The companion package that provides all the datasets used in the book "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).

This package provides functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.

Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.

Allows wrapping values in success() and failure() types to capture the result of operations, along with any status codes. Risky expressions can be wrapped in as_result() and functions wrapped in result() to catch errors and assign the relevant result types. Monadic functions can be bound together as pipelines or transaction scripts using then_try(), to gracefully handle errors at any step.