This package provides a screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects. This package also provides two screening functions for screening high-dimensional predictors when the events are rare. The firth method is called Rare-Screening which employs a repeated random sampling with replacement and using linear modeling with Bayes adjustment. The Second method is called Firth-ttScreening which uses ttScreening method with additional Firth correction term in the maximum likelihood for the logistic regression model. These methods handle the high-dimensionality and low event rates.

Collection of shiny widgets to support teal applications. Enables the manipulation of application layout and plot or table settings.

Implement text and sentiment analysis with texter'. Generate sentiment scores on text data and also visualize sentiments. texter allows you to quickly generate insights on your data. It includes support for lexicons such as NRC and Bing'.

This package implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.

This package contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliability and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures. Implements <https://www.fhwa.dot.gov/tpm/guidance/pm3_hpms.pdf>.

This package provides a wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.

An implementation that combines trait data and a phylogenetic tree (or trees) into a single object of class treedata.table'. The resulting object can be easily manipulated to simultaneously change the trait- and tree-level sampling. Currently implemented functions allow users to use a data.table syntax when performing operations on the trait dataset within the treedata.table object. For more details see Roman-Palacios et al. (2021) <doi:10.7717/peerj.12450>.

Package designed for working with vectors and lists of vectors, mainly for turning them into other indexed data structures.

This package provides data frames for forest or tree data structures. You can create forest data structures from data frames and process them based on their hierarchies.

Includes functions for mapping named lists to function arguments, random strings, pasting and combining rows together across columns, etc.

Runs tests using the testthat package but allows for multiple attempts for a single test. This is useful for noisy or flaky tests that generally pass but can fail due to occasional random errors, such as numeric instability or using random data.

Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.

This package provides functions for analyzing citizens bicycle usage pattern and predicting rental amount on specific conditions. Functions on this package interacts with data on tashudata package, a drat repository. tashudata package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. To install this data package, see the instructions at <https://github.com/zeee1/Tashu_Rpackage>. top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths. daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental. create_train_dataset(), create_test_dataset() is data processing function for prediction. Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset. Users can make random-forest prediction model by using create_train_model() and predict amount of bicycle rental in 2015 by using predict_bike_rental().

Unicodes are not friendly to work with, and not all Unicodes are Emoji per se, making obtaining Emoji statistics a difficult task. This tool can help your experience of working with Emoji as smooth as possible, as it has the tidyverse style.

Framework provides functions to parse Training Center XML (TCX) files and extract key activity metrics such as total distance, total time, calories burned, maximum altitude, and power values (watts). This package is useful for analyzing workout and training data from devices that export TCX format.

Tidying functions built on data.table to provide quick and efficient data manipulation with minimal overhead.

This package implements two-mode clustering (biclustering) using genetic algorithms. The method was first introduced in Hageman et al. (2008) <doi:10.1007/s11306-008-0105-7>. The package provides tools for fitting, visualization, and validation of two-mode cluster structures in data matrices.

Data handling and estimation functions for animal movement estimation from archival or satellite tags. Helper functions are included for making image summaries binned by time interval from Markov Chain Monte Carlo simulations.

This package produces weighted cross-tabulation tables for one or more outcome variables across one or more breakdown variables, and exports them directly to Excel'. For each outcome-by-breakdown combination, the package creates a weighted percentage table and a corresponding unweighted count table, with transparent handling of missing values and light, readable formatting. Designed to support social survey analysis workflows that require large sets of consistent, publication-ready tables.

The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on keras and tensorflow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

This package contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.

This package provides functions for the selection of thresholds for use in extreme value models, based mainly on the methodology in Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>. It also performs predictive inferences about future extreme values, based either on a single threshold or on a weighted average of inferences from multiple thresholds, using the revdbayes package <https://cran.r-project.org/package=revdbayes>. At the moment only the case where the data can be treated as independent identically distributed observations is considered.

Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) <doi:10.1186/1752-0509-4-121>.

Plot official statistics time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. tstools comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, tstools provides a super fast, data.table backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.