This package provides a dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles. The fpop package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017 <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error for a penalty linear in the number of changepoints.

Shiny app for the fdapace package.

This package provides a bundle of analytics tools for fisheries scientists. A shiny R App is included for a no-code solution for retrieval, analysis, and visualization.

This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.

Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>. Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection.

Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations). For details see Kinsvater et al. (2017) <arXiv:1701.06455>.

The lipid scrambling activity of protein extracts and purified scramblases is often determined using a fluorescence-based assay involving many manual steps. flippant offers an integrated solution for the analysis and publication-grade graphical presentation of dithionite scramblase assays, as well as a platform for review, dissemination and extension of the strategies it employs. The package's name derives from a play on the fact that lipid scrambling is also sometimes referred to as flipping'. The package is originally published as Cotton, R.J., Ploier, B., Goren, M.A., Menon, A.K., and Graumann, J. (2017). "flippantâ An R package for the automated analysis of fluorescence-based scramblase assays." BMC Bioinformatics 18, 146. <DOI:10.1186/s12859-017-1542-y>.

Parses financial condition and performance data (Call Reports) for institutions in the United States Farm Credit System. Contains functions for downloading files from the Farm Credit Administration (FCA) Call Report archive website and reading the files into tidy data frame format. The archive website can be found at <https://www.fca.gov/bank-oversight/call-report-data-for-download>.

This package creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the fixest package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <doi:10.48550/arXiv.2307.00369>.

Annotates Finnish textual survey responses into CoNLL-U format using Finnish treebanks from <https://universaldependencies.org/format.html> using UDPipe as described in Straka and Straková (2017) <doi:10.18653/v1/K17-3009>. Formatted data is then analysed using single or comparison n-gram plots, wordclouds, summary tables and Concept Network plots. The Concept Network plots use the TextRank algorithm as outlined in Mihalcea, Rada & Tarau, Paul (2004) <https://aclanthology.org/W04-3252/>.

This package provides functions for fitting data to a quiescent growth model, i.e. a growth process that involves members of the population who stop dividing or propagating.

This package provides functions that calculates common types of splitting criteria used in random forests for classification problems, as well as functions that make predictions based on a single tree or a Forest-R.K. model; the package also provides functions to generate importance plot for a Forest-R.K. model, as well as the 2D multidimensional-scaling plot of data points that are colour coded by their predicted class types by the Forest-R.K. model. This package is based on: Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) "Forest-R.K.: A New Random Forest Induction Method", Fourth International Conference on Intelligent Computing, September 2008, Shanghai, China, pp.430-437.

Implementation of two sample comparison procedures based on median-based statistical tests for functional data, introduced in Smida et al (2022) <doi:10.1080/10485252.2022.2064997>. Other competitive state-of-the-art approaches proposed by Chakraborty and Chaudhuri (2015) <doi:10.1093/biomet/asu072>, Horvath et al (2013) <doi:10.1111/j.1467-9868.2012.01032.x> or Cuevas et al (2004) <doi:10.1016/j.csda.2003.10.021> are also included in the package, as well as procedures to run test result comparisons and power analysis using simulations.

We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) <doi:10.4310/SII.2013.v6.n2.a1>.

An interface to the fastText <https://github.com/facebookresearch/fastText> library for efficient learning of word representations and sentence classification. The fastText algorithm is explained in detail in (i) "Enriching Word Vectors with subword Information", Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, 2017, <doi:10.1162/tacl_a_00051>; (ii) "Bag of Tricks for Efficient Text Classification", Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov, 2017, <doi:10.18653/v1/e17-2068>; (iii) "FastText.zip: Compressing text classification models", Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, Tomas Mikolov, 2016, <doi:10.48550/arXiv.1612.03651>.

This package provides a lightweight suite of functions for retrieving information about 5-digit or 2-digit US FIPS codes.

Easily use Font Awesome icons as shiny favicons (the icons that appear on browser tabs). Font Awesome (<https://fontawesome.com/>) is a popular set of icons that can be used in web pages. favawesome provides a simple way to use these icons as favicons in shiny applications and other HTML pages.

The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) <doi:10.1007/s11222-011-9249-9>, is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided.

This package provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the RcppNumerical package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference.

This package provides a toolkit for calculating forest and canopy structural complexity metrics from terrestrial LiDAR (light detection and ranging). References: Atkins et al. 2018 <doi:10.1111/2041-210X.13061>; Hardiman et al. 2013 <doi:10.3390/f4030537>; Parker et al. 2004 <doi:10.1111/j.0021-8901.2004.00925.x>.

Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering, hard-thresholding and soft- thresholding. The tuning parameters of these regularized estimators are selected via cross-validation.

This package provides functions for converting decimals to a matrix of numerators and denominators or a character vector of fractions. Supports mixed or improper fractions, finding common denominators for vectors of fractions, limiting denominators to powers of ten, and limiting denominators to a maximum value. Also includes helper functions for finding the least common multiple and greatest common divisor for a vector of integers. Implemented using C++ for maximum speed.

Visualise sequential distributions using a range of plotting styles. Sequential distribution data can be input as either simulations or values corresponding to percentiles over time. Plots are added to existing graphic devices using the fan function. Users can choose from four different styles, including fan chart type plots, where a set of coloured polygon, with shadings corresponding to the percentile values are layered to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.

Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the seqDesign package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results.