This package provides functions to download and parse information from INEGI (Official Mexican statistics agency). To learn more about the API, see <https://www.inegi.org.mx/servicios/api_indicadores.html>.

Implementation of functions to assign corresponding common job prestige scores (SIOPS, ISEI), the official job or group title and the ISCO-88 code to given ISCO-08 codes. ISCO-08 is the latest version of the International Standard Classification of Occupations which is used to organise information on labour and jobs.

Four datasets are provided here from the Intendo game Super Jetroid'. It is data from the 2015 year of operation and it comprises a revenue table ('all_revenue'), a daily users table ('users_daily'), a user summary table ('user_summary'), and a table with data on all user sessions ('all_sessions'). These core datasets come in different sizes, and, each of them has a variant that was intentionally made faulty (totally riddled with errors and inconsistencies). This suite of tables is useful for testing with packages that focus on data validation and data documentation.

This package provides user-friendly and configurable print debugging via a single function, ic(). Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code.

This package provides composable invertible transforms for (sparse) matrices.

Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.

Implementation of analytical and sampling-based power analyses for the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied to item response theory (IRT) models that are fitted using marginal maximum likelihood estimation. The methods are described in our paper (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).

Cluster sampling is a valuable approach when constructing a comprehensive list of individual units is challenging. It provides operational and cost advantages. This package is designed to test the efficiency of cluster sampling in terms cluster variance and design effect in context to crop surveys. This package has been developed using the algorithm of Iqbal et al. (2018) <doi:10.19080/BBOAJ.2018.05.555673>.

This package provides utility functions to deal with Italian fiscal code ('codice fiscale').

The methods in this package adds to the functionality of the intamap package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.

This package provides a key-value store data structure. The keys are integers and the values can be any R object. This is like a list but indexed by a set of integers, not necessarily contiguous and possibly negative. The implementation uses a R6 class. These containers are not faster than lists but their usage can be more convenient for certain situations.

Convert files to and from IDX format to vectors, matrices and arrays. IDX is a very simple file format designed for storing vectors and multidimensional matrices in binary format. The format is described on the website from Yann LeCun <http://yann.lecun.com/exdb/mnist/>.

Check if an externalptr is a null pointer. R does currently not have a native function for that purpose. This package contains a C function that returns TRUE in case of a null pointer.

This package provides a novel machine learning method for plant viruses diagnostic using genome sequencing data. This package includes three different machine learning models, random forest, XGBoost, and elastic net, to train and predict mapped genome samples. Mappability profile and unreliable regions are introduced to the algorithm, and users can build a mappability profile from scratch with functions included in the package. Plotting mapped sample coverage information is provided.

The general workflow of most imputation methods is quite similar. The aim of this package is to provide parts of this general workflow to make the implementation of imputation methods easier. The heart of an imputation method is normally the used model. These models can be defined using the parsnip package or customized specifications. The rest of an imputation method are more technical specification e.g. which columns and rows should be used for imputation and in which order. These technical specifications can be set inside the imputation functions.

It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecologà a AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.

Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.

As a sequel to iNEXT', the iNEXT.beta3D package provides functions to compute standardized taxonomic, phylogenetic, and functional diversity (3D) estimates with a common sample size (for alpha and gamma diversity) or sample coverage (for alpha, beta, gamma diversity as well as dissimilarity or turnover indices). Hill numbers and their generalizations are used to quantify 3D and to make multiplicative decomposition (gamma = alpha x beta). The package also features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of beta diversity across datasets. See Chao et al. (2023) <doi:10.1002/ecm.1588> for more details.

Tools, tutorials, and demos of Item Factor Analysis using OpenMx'. This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.

Implementation of the information matrix test for generalized partial credit models.

Note that imageData has been superseded by growthPheno'. The package growthPheno incorporates all the functionality of imageData and has functionality not available in imageData', but some imageData functions have been renamed. The imageData package is no longer maintained, but is retained for legacy purposes.

An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.

This package provides a fast (C) implementation of the iterative proportional fitting procedure.