Compute ploidy of single cells (or nuclei) based on single-cell (or single-nucleus) ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) data <https://github.com/fumi-github/scPloidy>.

Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.

Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.

Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book.

An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems).

Design, build, and deploy R packages demo presentations by an interactive wizard. Set up unique title, logo and themes. Add personalized tabs exposing applicability. And deploy as a part of a package or an independent app.

This package implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides statistical methods based on the SWDFT, and graphical tools to display the outputs.

The StockDistFit package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) <https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and Benth et al. (2008) <https://books.google.co.ke/books?hl=en&lr=&id=MHNpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>.

It's my experience that working with shiny is intuitive once you're into it, but can be quite daunting at first. Several common mistakes are fairly predictable, and therefore we can control for these. The functions in this package help match up the assets listed in the UI and the SERVER files, and Visualize the ad hoc structure of the shiny App.

This package provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See ?SpaDES.tools for an categorized overview of these additional tools. The suggested package NLMR can be installed from the following repository: (<https://PredictiveEcology.r-universe.dev>).

Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers.

Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The splithalfr supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals.

This package provides a major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().

This package provides functions for small area estimation.

Convert text (and text in R objects) to Mocking SpongeBob case <https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun ways. CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem OFf IN Fun WayS.

Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.

Forms queries to submit to the Cleveland Federal Reserve Bank web site's financial stress index data site. Provides query functions for both the composite stress index and the components data. By default the download includes daily time series data starting September 25, 1991. The functions return a class of either type easing or cfsi which contain a list of items related to the query and its graphical presentation. The list includes the time series data as an xts object. The package provides four lattice time series plots to render the time series data in a manner similar to the bank's own presentation.

This package provides a mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.

Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++.

User tools for working with The STOICH (Stoichiometric Traits of Organisms in their Chemical Habitats) Project database <https://snr-stoich.unl.edu/>. This package is designed to aid in data discovery, filtering, pairing water samples with organism samples, and merging data tables to assist users in preparing data for analyses. For additional examples see "Additional Examples" and the readme file at <https://github.com/STOICH-project/STOICH-utilities>.

This package implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains.

Selective sweep is a biological phenomenon in which genetic variation between neighboring beneficial mutant alleles is swept away due to the effect of genetic hitchhiking. Detection of selective sweep is not well acquainted as well as it is a laborious job. This package is a user friendly approach for detecting selective sweep in genomic regions. It uses a Random Forest based machine learning approach to predict selective sweep from VCF files as an input. Input of this function, train data and new data, can be computed using the project <https://github.com/AbhikSarkar1999/SweepDiscovery> in GitHub'. This package has been developed by using the concept of Pavlidis and Alachiotis (2017) <doi:10.1186/s40709-017-0064-0>.

Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework as described in Muñoz-Pajares (2013) <doi:10.1111/2041-210X.12118>.

An R API providing access to a relational database with macroeconomic time series data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData <https://www.econdata.co.za/> platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University.