Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

Pleiotropy-informed significance analysis of genome-wide association studies with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes factor for post-GWAS analyses (e.g., fine mapping and colocalization).

This package provides a collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) <https://stevehanov.ca/blog/index.php?id=114>.

Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.

This package provides an implementation of simplicial complexes for Topological Data Analysis (TDA). The package includes functions to compute faces, boundary operators, Betti numbers, Euler characteristic, and to construct simplicial complexes. It also implements persistent homology, from building filtrations to computing persistence diagrams, with the aim of helping readers understand the core concepts of computational topology. Methods are based on standard references in persistent homology such as Zomorodian and Carlsson (2005) <doi:10.1007/s00454-004-1146-y> and Chazal and Michel (2021) <doi:10.3389/frai.2021.667963>.

Powerful graphical displays and statistical tools for structured problem solving and diagnosis. The functions of the sherlock package are especially useful for applying the process of elimination as a problem diagnosis technique. The sherlock package was designed to seamlessly work with the tidyverse set of packages and provides a collection of graphical displays built on top of the ggplot and plotly packages, such as different kinds of small multiple plots as well as helper functions such as adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file. References: David Hartshorne (2019, ISBN: 978-1-5272-5139-7). Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467).

This package performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression.

This package provides functions to calculate step- and cadence-based metrics from timestamped accelerometer and wearable device data. Supports CSV and AGD files from ActiGraph devices, CSV files from Fitbit devices, and step counts derived with R package GGIR <https://github.com/wadpac/GGIR>, with automatic handling of epoch lengths from 1 to 60 seconds. Metrics include total steps, cadence peaks, minutes and steps in predefined cadence bands, and time and steps in moderate-to-vigorous physical activity (MVPA). Methods and thresholds are informed by the literature, e.g., Tudor-Locke and Rowe (2012) <doi:10.2165/11599170-000000000-00000>, Barreira et al. (2012) <doi:10.1249/MSS.0b013e318254f2a3>, and Tudor-Locke et al. (2018) <doi:10.1136/bjsports-2017-097628>. The package record is also available on Zenodo (2023) <doi:10.5281/zenodo.7858094>.

Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1).

Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.

This package provides a simple function that anonymises a list of variables in a consistent way: anonymised factors are not recycled and the same original levels receive the same anonymised factor even if located in different datasets.

Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement.

Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>. Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.

This package provides a simple wrapper to easily design vanilla deep neural networks using Tensorflow'/'Keras backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection).

Because your linear models deserve better than console output. A sleek color palette and kable styling to make your regression results look sharper than they are. Includes support for Partial Least Squares (PLS) regression via both the SVD and NIPALS algorithms, along with a unified interface for model fitting and fabulous LaTeX and console output formatting. See the package website at <https://finitesample.space/snazzier>.

This package provides a rich set of UI components for building Shiny applications, including inputs, containers, overlays, menus, and various utilities. All components from Fluent UI (the underlying JavaScript library) are available and have usage examples in R.

This package provides a collection of tools for analyzing significance of assets, funds, and trading strategies, based on the Sharpe ratio and overfit of the same. Provides density, distribution, quantile and random generation of the Sharpe ratio distribution based on normal returns, as well as the optimal Sharpe ratio over multiple assets. Computes confidence intervals on the Sharpe and provides a test of equality of Sharpe ratios based on the Delta method. The statistical foundations of the Sharpe can be found in the author's Short Sharpe Course <doi:10.2139/ssrn.3036276>.

Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.

Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) <arXiv:2002.05746>. This package provides methods for fitting ITS models with lagged outcomes and variables to account for temporal dependencies. It then conducts inference via simulation, simulating a set of plausible counterfactual post-policy series to compare to the observed post-policy series. This package also provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. This work partially funded by Arnold Ventures in collaboration with MDRC.

Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.

Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.

In population management, data come at more or less regular intervals over time in sampling batches (bouts) and decisions should be made with the minimum number of samples and as quickly as possible. This package provides tools to implement, produce charts with stop lines, summarize results and assess sequential analyses that test hypotheses about population sizes. Two approaches are included: the sequential test of Bayesian posterior probabilities (Rincon, D.F. et al. 2025 <doi:10.1111/2041-210X.70053>), and the sequential probability ratio test (Wald, A. 1945 <http://www.jstor.org/stable/2235829>).

Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.

This package provides a tool for working with SQLite databases. SQLite has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, SQLite doesn't have a date type and sqliteutils has some functions to deal with that.