This package provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.

Software to simulate population change across space and time. Visintin et al. (2020) <doi:10.1111/2041-210X.13354>.

The steepness package computes steepness as a property of dominance hierarchies. Steepness is defined as the absolute slope of the straight line fitted to the normalized David's scores. The normalized David's scores can be obtained on the basis of dyadic dominance indices corrected for chance or by means of proportions of wins. Given an observed sociomatrix, it computes hierarchy's steepness and estimates statistical significance by means of a randomization test.

Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.

Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.

This package implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

The goal of safejoin is to guarantee that when performing joins extra rows are not added to your data. safejoin provides a wrapper around dplyr::left_join that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.

The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.

Classical methods for combining summary data from genome-wide association studies (GWAS) only use marginal genetic effects and power can be compromised in the presence of heterogeneity. subgxe is a R package that implements p-value assisted subset testing for association (pASTA), a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA generalizes association analysis based on subsets by incorporating gene-environment interactions into the testing procedure.

Produce small area population estimates by fitting census data to survey data.

Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.

Simulation tools for closed-loop simulation are provided for the MSEtool operating model to inform data-rich fisheries. SAMtool provides a conditioning model, assessment models of varying complexity with standardized reporting, model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.

This package performs canonical correlation for survey data, including multiple tests of significance for secondary canonical correlations. A key feature of this package is that it incorporates survey data structure directly in a novel test of significance via a sequence of simple linear regression models on the canonical variates. See reference - Cruz-Cano, Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" The R Journal under review.

The SAWNUTI algorithm performs sequence comparison for finite sequences of discrete events with non-uniform time intervals. Further description of the algorithm can be found in the paper: A. Murph, A. Flynt, B. R. King (2021). Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. <doi:10.1080/07474946.2021.1940491>.

Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions.

This package provides a set of tools to assist statistical programmers in validating Study Data Tabulation Model (SDTM) domain data sets. Statistical programmers are required to validate that a SDTM data set domain has been programmed correctly, per the SDTM Implementation Guide (SDTMIG) by CDISC (<https://www.cdisc.org/standards/foundational/sdtmig>), study specification, and study protocol using a process called double programming. Double programming involves two different programmers independently converting the raw electronic data cut (EDC) data into a SDTM domain data table and comparing their results to ensure accurate standardization of the data. One of these attempts is termed production and the other validation'. Generally, production runs are the official programs for submittals and these are written in SAS'. Validation runs can be programmed in another language, in this case R'.

This package contains space filling based tools for machine learning and data mining. Some functions offer several computational techniques and deal with the out of memory for large big data by using the ff package.

Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <doi:10.48550/arXiv.0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

This is a compilation of my preferred themes and related theme elements for ggplot2'. I believe these themes and theme elements are aesthetically pleasing, both for pedagogical instruction and for the presentation of applied statistical research to a wide audience. These themes imply routine use of easily obtained/free fonts, simple forms of which are included in this package.

Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches.

Cleans and formats language transcripts guided by a series of transformation options (e.g., lemmatize words, omit stopwords, split strings across rows). SemanticDistance computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. SemanticDistance can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). SemanticDistance includes visualization options for analyzing distances as time series data and simple semantic network dynamics (e.g., clustering, undirected graph network).

This package provides a novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. seer package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.

Generates a random quotation from a database of quotes on topics in statistics, data visualization and science. Other functions allow searching the quotes database by key term tags, or authors or creating a word cloud. The output is designed to be suitable for use at the console, in Rmarkdown and LaTeX.