Compare Models with Cross-Validated Log-Likelihood

An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) (see also Harden and Desmarais, 2011 ) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) . These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) and Joshi & Mason (2008) that are included to facilitate examples from real-world analysis.

Compare the Goodness of Fit of Benford's and Blondeau Da Silva's Digit Distributions to a Given Dataset

Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).

Longitudinal Graphical Lasso

For high-dimensional correlated observations, this package carries out the L_1 penalized maximum likelihood estimation of the precision matrix (network) and the correlation parameters. The correlated data can be longitudinal data (may be irregularly spaced) with dampening correlation or clustered data with uniform correlation. For the details of the algorithms, please see the paper Jie Zhou et al. Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence data .

Yeast-Proteome Secondary-Structure Calculator

An extension for 'NetSurfP-2.0' (Klausen et al. (2019) ) which is specifically designed to analyze the results of bottom-up-proteomics that is primarily analyzed with 'MaxQuant' (Cox, J., Mann, M. (2008) ). This tool is designed to process a large number of yeast peptides that produced as a results of whole yeast cell-proteome digestion and provide a coherent picture of secondary structure of proteins.

Analyse Biological Invasion Costs with the 'InvaCost' Database

Provides an up-to-date version of the 'InvaCost' database () in R, and several functions to analyse the costs of invasive alien species ().

Find Related Items and Lexical Dimensions in a Lexicon

Implements code to identify lexical competitors in a given list of words. We include many of the standard competitor types used in spoken word recognition research, such as functions to find cohorts, neighbors, and rhymes, amongst many others. The package includes documentation for using a variety of lexicon files, including those with form codes made up of multiple letters (i.e., phoneme codes) and also basic orthographies. Importantly, the code makes use of multiple CPU cores and vectorization when possible, making it extremely fast and able to handle large lexicons. Additionally, the package contains documentation for users to easily write new functions, allowing researchers to examine other relationships within a lexicon. Preprint: < https://osf.io/preprints/psyarxiv/8dyru/>. Open access: . Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). .

Functions Related to ICES Advice

A collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics.

Read and Write Standard 'C' Types from Files, Connections and Raw Vectors

Interacting with binary files can be difficult because R's types are a subset of what is generally supported by 'C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using 'C' type descriptions. These functions convert data between 'C' types and R types while checking for values outside the type limits, 'NA' values, etc.

Root Expected Proportion Squared Difference for Detecting DIF

Root Expected Proportion Squared Difference (REPSD) is a nonparametric differential item functioning (DIF) method that (a) allows practitioners to explore for DIF related to small, fine-grained focal groups of examinees, and (b) compares the focal group directly to the composite group that will be used to develop the reported test score scale. Using your provided response matrix with a column that identifies focal group membership, this package provides the REPSD values, a simulated null distribution of possible REPSD values, and the simulated p-values identifying items possibly displaying DIF without requiring enormous sample sizes.

Efficient Voting Methods for Committee Selection

A fast 'Rcpp'-based implementation of polynomially-computable voting theory methods for committee ranking and scoring. The package includes methods such as Approval Voting (AV), Satisfaction Approval Voting (SAV), sequential Proportional Approval Voting (PAV), and sequential Phragmen's Rule. Weighted variants of these methods are also provided, allowing for differential voter influence.