SHE, FORAM Index and ABC Method analyses and custom plot functions for community data.

Fast reverse complement of DNA and RNA sequences using a C++ lookup table for O(1) per-base complement mapping. Supports full IUPAC ambiguity codes, DNA and RNA modes, case preservation, and NA handling. Much faster than other packages for computing reverse complements of many short sequences such as primers, probes, and, k-mers.

This package provides easy-to-understand and consistent interfaces for accessing data on the U.S. Congress. The functions in filibustr streamline the process for importing data on Congress into R, removing the need to download and work from CSV files and the like. Data sources include Voteview (<https://voteview.com/>), the U.S. Senate website (<https://www.senate.gov/>), and more.

Recent technological advances have enable the simultaneous collection of multi-omics data i.e., different types or modalities of molecular data, presenting challenges for integrative prediction modeling due to the heterogeneous, high-dimensional nature and possible missing modalities of some individuals. We introduce this package for late integrative prediction modeling, enabling modality-specific variable selection and prediction modeling, followed by the aggregation of the modality-specific predictions to train a final meta-model. This package facilitates conducting late integration predictive modeling in a systematic, structured, and reproducible way.

Interface for accessing the Fortnite Ecosystem API, allowing users to retrieve island metadata and engagement metrics. The package provides functions to search for Fortnite Creative islands, retrieve detailed metadata about specific islands including titles, descriptions, and tags, and access engagement metrics such as daily active users and play duration. It supports pagination for large result sets and time-series analysis of island performance. The API endpoint is <https://api.fortnite.com/ecosystem/v1>.

This package provides a collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.

Spatio-temporal locations of an animal are computed from annotated data with a hidden Markov model via particle filter algorithm. The package is relatively robust to varying degrees of shading. The hidden Markov model is described in Movement Ecology - Rakhimberdiev et al. (2015) <doi:10.1186/s40462-015-0062-5>, general package description is in the Methods in Ecology and Evolution - Rakhimberdiev et al. (2017) <doi:10.1111/2041-210X.12765> and package accuracy assessed in the Journal of Avian Biology - Rakhimberdiev et al. (2016) <doi:10.1111/jav.00891>.

In order to achieve accurate estimation without sparsity assumption on the precision matrix, element-wise inference on the precision matrix, and joint estimation of multiple Gaussian graphical models, a novel method is proposed and efficient algorithm is implemented. FLAG() is the main function given a data matrix, and FlagOneEdge() will be used when one pair of random variables are interested where their indices should be given. Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications, see Qian Y (2023) <doi:10.14711/thesis-991013223054603412>, Qian Y, Hu X, Yang C (2023) <doi:10.48550/arXiv.2306.17584>.

Implementation of the FASSTER (Forecasting with Additive Switching of Seasonality, Trend, and Exogenous Regressors) model for forecasting time series with multiple seasonal patterns. The model combines state space methodology with a switching component in the observation equation to allow flexible modeling of complex seasonal patterns, including time-varying effects and multiple seasonalities.

Miscellaneous utilities, tools and helper functions for finding and searching files on disk, searching for and removing R objects from the workspace. Does not import or depend on any third party package, but on core R only (i.e. it may depend on packages with priority base').

This package implements fast, scalable optimization algorithms for fitting generalized principal components analysis (GLM-PCA) models, as described in "A Generalization of Principal Components Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE (2002, ISBN:9780262271738), and subsequently "Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.

Designed to streamline the process of analyzing genotyping data from Fluidigm machines, this package offers a suite of tools for data handling and analysis. It includes functions for converting Fluidigm data to format used by PLINK', estimating errors, calculating pairwise similarities, determining pairwise similarity loci, and generating a similarity matrix.

This package provides a neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" <doi:10.1080/10618600.2023.2204136>.

Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.

Wrapper functions around the Facebook Marketing API to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.

Flexible framework for specifying survival distributions through their hazard (failure rate) functions. Define arbitrary time-varying hazard functions to model complex failure patterns including bathtub curves, proportional hazards with covariates, and other non-standard hazard behaviors. Provides automatic computation of survival, CDF, PDF, quantiles, and sampling. Implements the likelihood model interface for maximum likelihood estimation with right-censored and left-censored survival data.

An easy-to-use web client/wrapper for the Figma API <https://www.figma.com/developers/api>. It allows you to bring all data from a Figma file to your R session. This includes the data of all objects that you have drawn in this file, and their respective canvas/page metadata.

Reads and writes ARFF files. ARFF (Attribute-Relation File Format) files are like CSV files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the WEKA machine learning Java toolbox. See <https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/> for further info on ARFF and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on WEKA'. farff gets rid of the Java dependency that RWeka enforces, and it is at least a faster reader (for bigger files). It uses readr as parser back-end for the data section of the ARFF file. Consistency with RWeka is tested on Github and Travis CI with hundreds of ARFF files from OpenML'.

This package provides methods to compute simultaneous prediction and confidence bands for dense time series data. The implementation builds on the functional bootstrap approach proposed by Lenhoff et al. (1999) <doi:10.1016/S0966-6362(98)00043-5> and extended by Koska et al. (2023) <doi:10.1016/j.jbiomech.2023.111506> to support both independent and clustered (hierarchical) data. Includes a simple API (see band()) and an Rcpp backend for performance.

This package provides utilities to facilitate handling of Fude Polygon data downloadable from the Ministry of Agriculture, Forestry and Fisheries website <https://open.fude.maff.go.jp>.

Around 10% of almost any predictive modeling project is spent in predictive modeling, funModeling and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables dataViz', assessing model performance and other functions.

Compare variables of interest between (potentially large numbers of) spatial interactions and meta-variables. Spatial variables are summarized using K, or other, functions, and projected for use in a modified random forest model. The model allows comparison of functional and non-functional variables to each other and to noise, giving statistical significance to the results. Included are preparation, modeling, and interpreting tools along with example datasets, as described in VanderDoes et al., (2023) <doi:10.1101/2023.07.18.549619>.

Access small example datasets from Luquillo, a ForestGEO site in Puerto Rico (<https://forestgeo.si.edu/sites/north-america/luquillo>).

An implementation in Rcpp / RcppArmadillo of Partial Least Square algorithms. This package includes other functions to perform the double cross-validation and a fast correlation.