Time Series Segmented Residual Trends is a method for the automated detection of land degradation from remotely sensed vegetation and climate datasets. TSS-RESTREND incorporates aspects of two existing degradation detection methods: RESTREND which is used to control for climate variability, and BFAST which is used to look for structural changes in the ecosystem. The full details of the testing and justification of the TSS-RESTREND method (version 0.1.02) are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>. The changes to the method introduced in version 0.2.03 focus on the inclusion of temperature as an additional climate variable. This allows for land degradation assessment in temperature limited drylands. A paper that details this work is currently under review. There are also a number of bug fixes and speed improvements. Version 0.3.0 introduces additional attribution for eCO2, climate change and climate variability the details of which are in press in Burrell et al., (2020). The version under active development and additional example scripts showing how the package can be applied can be found at <https://github.com/ArdenB/TSSRESTREND>.

This package provides feedback about dplyr and tidyr operations.

Tests the hypothesis that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test. The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi10.1016/j.csda.2010.04.012.

This package provides tools for computing various vector summaries of persistence diagrams studied in Topological Data Analysis. For improved computational efficiency, all code for the vector summaries is written in C++ using the Rcpp and RcppArmadillo packages.

Plots ternary diagrams (simplex plots / Gibbs triangles) and Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the standard graphics functions. Allows custom annotation, interpolating, contouring and scaling of plotting region. Includes a Shiny user interface for point-and-click ternary plotting. An alternative to ggtern', which uses the ggplot2 family of plotting functions.

Data filtering module for teal applications. Allows for interactive filtering of data stored in data.frame and MultiAssayExperiment objects. Also displays filtered and unfiltered observation counts.

This package provides a framework for text cleansing and analysis. Conveniently prepare and process large amounts of text for analysis. Includes various metrics for word counts/frequencies that scale efficiently. Quickly analyze large amounts of text data using a text.table (a data.table created with one word (or unit of text analysis) per row, similar to the tidytext format). Offers flexibility to efficiently work with text data stored in vectors as well as text data formatted as a text.table.

Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>. Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod). Classical CA is based on the Euclidean distance. Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance. For some tables, Taxicab CA gives more informative results than classical CA.

Convert semi-structured log files (such as Apache access.log files) into a tabular format (data.frame) using a standard template system.

Approximations of global p-values when testing hypothesis in presence of non-identifiable nuisance parameters. The method relies on the Euler characteristic heuristic and the expected Euler characteristic is efficiently computed by in Algeri and van Dyk (2018) <arXiv:1803.03858>.

Simulation of random vectors from truncated multivariate normal and t distributions based on the algorithms proposed by Yifang Li and Sujit K. Ghosh (2015) <doi:10.1080/15598608.2014.996690>.

Carries out analyses of two-way tables with one observation per cell, together with graphical displays for an additive fit and a diagnostic plot for removable non-additivity via a power transformation of the response. It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a 1-degree-of-freedom test for row*column non-additivity', linear in the row and column effects.

Utilities for text analysis.

Instance feature calculation and evolutionary instance generation for the traveling salesman problem. Also contains code to "morph" two TSP instances into each other. And the possibility to conveniently run a couple of solvers on TSP instances.

Defines the classes used to identify outliers (threshing) and compute the number of significant principal components and number of clusters (reaping) in a joint application of PCA and hierarchical clustering. See Wang et al., 2018, <doi:10.1186/s12859-017-1998-9>.

This package provides two classes extending data.table class. Simple tableList class wraps data.table and any additional structures together. More complex tableMatrix class combines data.table and matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more information and examples.

Facilitate the movement between data frames to xts'. Particularly useful when moving from tidyverse to the widely used xts package, which is the input format of choice to various other packages. It also allows the user to use a spread_by argument for a character column xts conversion.

This package provides a suite of descriptive and inferential methods designed to evaluate one or more biomarkers for their ability to guide patient treatment recommendations. Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) <doi:10.1515/ijb-2012-0052> for further details.

Computation of stopping boundaries for a single-arm trial using a Bayesian criterion. For each m<=n (n=total patient number of the trial) the smallest number of observed toxicities is calculated leading to the termination of the trial/accrual according to the specified criteria. The probabilities of stopping the trial/accrual at and up until (resp.) the m-th patient (m<=n) is also calculated. This design is more conservative than the frequentist approach (using Clopper Pearson CIs) which might be preferred as it concerns safety. See also Aamot et al. (2010) "Continuous monitoring of toxicity in clinical Trials - simulating the risk of stopping prematurely" <doi:10.5414/cpp48476>.

Use the <https://toggl.com> time tracker api through R.

The general principle relies on calculating the cumulative signal of nascent RNA sequencing over the gene body of any given gene or transcription unit. tepr can identify transcription attenuation sites by comparing profile to a null model which assumes uniform read density over the entirety of the transcription unit. It can also identify increased or diminished transcription attenuation by comparing two conditions. Besides rigorous statistical testing and high sensitivity, a major feature of tepr is its ability to provide the elongation pattern of each individual gene, including the position of the main attenuation point when such a phenomenon occurs. Using tepr', users can visualize and refine genome-wide aggregated analyses of elongation patterns to robustly identify effects specific to subsets of genes. These metrics are suitable for internal comparisons (between genes in each condition) and for studying elongation of the same gene in different conditions or comparing it to a perfect theoretical uniform elongation.

Visualize your Tidyverse data analysis pipelines via the Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.

Suite of tools to support the practice of tada science. It includes an engaging package roulette that is designed to facilitate learning about new packages.

Reconstruct phylogenetic trees from discrete data. Inapplicable character states are handled using the algorithm of Brazeau, Guillerme and Smith (2019) <doi:10.1093/sysbio/syy083> with the "Morphy" library, under equal or implied step weights. Contains a "shiny" user interface for interactive tree search and exploration of results, including character visualization, rogue taxon detection, tree space mapping, and cluster consensus trees (Smith 2022a, b) <doi:10.1093/sysbio/syab099>, <doi:10.1093/sysbio/syab100>. Profile Parsimony (Faith and Trueman, 2001) <doi:10.1080/10635150118627>, Successive Approximations (Farris, 1969) <doi:10.2307/2412182> and custom optimality criteria are implemented.