Publication-ready regional gene locus plots similar to those produced by the web interface LocusZoom <https://my.locuszoom.org>, but running locally in R. Genetic or genomic data with gene annotation tracks are plotted via R base graphics, ggplot2 or plotly', allowing flexibility and easy customisation including laying out multiple locus plots on the same page. It uses the LDlink API <https://ldlink.nih.gov/?tab=apiaccess> to query linkage disequilibrium data from the 1000 Genomes Project and can overlay this on plots <doi:10.1093/bioadv/vbaf006>.

Create tables from within R directly on Google Slides presentations. Currently supports matrix, data.frame and flextable objects.

Solves quadratic programming problems where the Hessian is represented as the product of two matrices. Thanks to Greg Hunt for helping getting this version back on CRAN. The methods in this package are described in: Ormerod, Wand and Koch (2008) "Penalised spline support vector classifiers: computational issues" <doi:10.1007/s00180-007-0102-8>.

This package provides a suite of functions for reading in a rate file in XML format, stratify a cohort, and calculate SMRs from the stratified cohort and rate file.

Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Based on FORTRAN program ld98 implemented by Reboussin, et al. (2000) <doi:10.1016/s0197-2456(00)00057-x>.

This package provides a stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removedâ ) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.

Letter Values for the course Exploratory Data Analysis at Federal University of Bahia (Brazil). The approach implemented in the package is presented in the textbook of Tukey (1977) <ISBN: 978-0201076165>.

Genome-wide association (GWAS) analyses of a biomarker that account for the limit of detection.

Fits look-up tables by filling entries with the mean or median values of observations fall in partitions of the feature space. Partitions can be determined by user of the package using input argument feature.boundaries, and dimensions of the feature space can be any combination of continuous and categorical features provided by the data set. A Predict function directly fetches corresponding entry value, and a default value is defined as the mean or median of all available observations. The table and other components are represented using the S4 class lookupTable.

Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.

Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading. Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, Market Type research of Van Tharp Institute: <https://vantharp.com/>, Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.

An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.

This package provides tools for performing Bayesian inference on epidemiological data to estimate the time-varying reproductive number and other related metrics. These methods were published in Li and White (2021) <doi:10.1371/journal.pcbi.1009210>. This package supports analyses based on aggregated case count data and individual line list data, facilitating enhanced surveillance and intervention planning for infectious diseases like COVID-19.

Mixture modelling of one-dimensional data using combinations of left-truncated Gamma, Weibull, and Lognormal Distributions. Blostein, Martin & Miljkovic, Tatjana. (2019) <doi:10.1016/j.insmatheco.2018.12.001>.

Extends the functionality of the tourr package by an interactive graphical user interface. The interactivity allows users to effortlessly refine their tourr results by manual intervention, which allows for integration of expert knowledge and aids the interpretation of results. For more information on tourr see Wickham et. al (2011) <doi:10.18637/jss.v040.i02> or <https://github.com/ggobi/tourr>.

This package provides in built datasets and three functions. These functions are mobility_index, nonStanTest and linkedLives. The mobility_index function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the linkedLives functions allow the user to determine the probability that the observed sequence data was due to chance. The linkedLives function acknowledges the fact that some individuals may have identical sequences. The datasets available provide sequence data on marital status(maritalData) and mobility (mydata) for a selected group of individuals from the British Household Panel Study (BHPS). In addition, personal and house ID's for 100 individuals are provided in a third dataset (myHouseID) from the BHPS.

This package provides methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the LatticeKrigRPackage GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research.

LP nonparametric high-dimensional K-sample comparison method that includes (i) confirmatory test, (ii) exploratory analysis, and (iii) options to output a data-driven LP-transformed matrix for classification. The primary reference is Mukhopadhyay, S. and Wang, K. (2020, Biometrika); <arXiv:1810.01724>.

This package provides tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.

Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.

Generates the Langa-Weir classification of cognitive function for the 2022 Health and Retirement Study (HRS) cognition data. It is particularly useful for researchers studying cognitive aging who wish to work with the most recent release of HRS data. The package provides user-friendly functions for data preprocessing, scoring, and classification allowing users to easily apply the Langa-Weir classification system. For details regarding the; HRS <https://hrsdata.isr.umich.edu/> and Langa-Weir classifications <https://hrsdata.isr.umich.edu/data-products/langa-weir-classification-cognitive-function-1995-2020>.

This package provides a variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

This package produces high resolution, publication ready linkage maps and quantitative trait loci maps. Input can be output from R/qtl', simple text or comma delimited files. Output is currently a portable document file.

Lights Out is a puzzle game consisting of a grid of lights that are either on or off. Pressing any light will toggle it and its adjacent lights. The goal of the game is to switch all the lights off. This package provides an interface to play the game on different board sizes, both through the command line or with a visual application. Puzzles can also be solved using the automatic solver included. View a demo online at <https://daattali.com/shiny/lightsout/>.