Compile snippets of LaTeX directly into images from the R console to view in the RStudio viewer pane, Shiny apps and RMarkdown documents.

Utilities for rapidly loading specified rows and/or columns of data from large tab-separated value (tsv) files (large: e.g. 1 GB file of 10000 x 10000 matrix). tsvio is an R wrapper to C code that creates an index file for the rows of the tsv file, and uses that index file to collect rows and/or columns from the tsv file without reading the whole file into memory.

This package provides tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.

This package provides an intuitive interface for working with the competing risk endpoints. The package wraps the cmprsk package, and exports functions for univariate cumulative incidence estimates and competing risk regression. Methods follow those introduced in Fine and Gray (1999) <doi:10.1002/sim.7501>.

This package provides functions and Examples in Sample Size Calculation in Clinical Research.

This package provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters.

Conditional logistic regression with longitudinal follow up and individual-level random coefficients: A stable and efficient two-step estimation method.

This package implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.

This package provides functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.

This package provides functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money. Raymond M. Brooks (â Financial Management,â 2018, ISBN: 9780134730417). Sheridan Titman, Arthur J. Keown, John D. Martin (â Financial Management: Principles and Applications,â 2017, ISBN: 9780134417219). Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (â Fundamentals of Corporate Finance,â 2019, ISBN: 9780134735313). S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (â Contemporary Business Mathematics with Canadian Applications,â 2020, ISBN: 9780135285015).

Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).

Allows users to analyze text and classify emotions such as happiness, sadness, anger, fear, and neutrality. It combines text preprocessing, TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction, and Random Forest classification to predict emotions and map them to corresponding emojis for enhanced sentiment visualization.

An interface to twitter-text', a JavaScript library which is responsible for determining the length/validity of a tweet and identifying/linking any URLs or special tags (e.g. mentions or hashtags) which may be present.

This package provides a toolkit for web scraping, modular NLP pipelines, and text preparation for large language models. Organized around four core actions: fetching, reading, processing, and searching. Covers the full pipeline from raw web data acquisition to structural text processing and BM25 indexing. Supports multiple retrieval strategies including regex, dictionary matching, and ranked keyword search. Pipe-friendly with no heavy dependencies; all outputs are plain data frames or data.tables.

There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010<doi:10.1080/15598608.2010.10412013>). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design.

This package provides a collection of functions for generating frequency tables and cross-tabulations of categorical variables. The resulting tables can be exported to various formats (Excel, PDF, HTML, etc.) with extensive formatting and layout customization options.

Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) <doi:10.18637/jss.v098.i15>.

This package provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the theft package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.

Uses read counts for biallelic single nucleotide polymorphisms (SNPs) to compare the likelihoods for the observed read counts given that a sample is either diploid or triploid. It allows parameters to be specified to account for sequencing error rates and allelic bias. For details of the algorithm, please see Delomas (2019) <doi:10.1111/1755-0998.13073>.

Interface to TensorFlow <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API'. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Data frame class for storing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software.

Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template.

Interface to TensorFlow Probability', a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', GPU'). TensorFlow Probability includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.

Save the output of statistical tests in an organized file that can be shared with others or used to report statistics in scientific papers.