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

This package provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

This package provides a curated reference list of countries and territories with classifications for Small Island Developing States (SIDS), sub-national island jurisdictions (SNIJ), World Bank region and income group, and political association. Sub-sovereign cases such as Aruba, Curacao, Bonaire, Sint Maarten, the French overseas territories, and Aaland Islands are represented with disambiguating codes that standard country-code packages often collapse or omit. Provides predicate helpers and a tidy joiner intended to extend rather than replace countrycode'. Source data is maintained at <https://github.com/University-of-Aruba/island-research-reference-data> and licensed CC BY 4.0.

This package provides a collection of Irucka Embry's miscellaneous USGS data sets (USGS Parameter codes with fixed values, USGS global time zone codes, and US Air Force Global Engineering Weather Data). Irucka created these data sets while a Cherokee Nation Technology Solutions (CNTS) United States Geological Survey (USGS) Contractor and/or USGS employee.

This package provides functions to make inference about the standardized mortality ratio (SMR) when evaluating the effect of a screening program. The package is based on methods described in Sasieni (2003) <doi: 10.1097/00001648-200301000-00026> and Talbot et al. (2011) <doi: 10.1002/sim.4334>.

This package provides a comprehensive toolkit for Interpretive Structural Modelling (ISM) analysis. Provides functions for creating adjacency matrices from various input formats including SSIM (Structural Self-Interaction Matrix), computing reachability matrices using Warshall's algorithm, performing hierarchical level partitioning, MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis, and visualizing ISM structures through both static and interactive diagrams. ISM is a methodology for identifying and summarizing relationships among specific elements which define an issue or problem, as described in Warfield (1974) <doi:10.1109/TSMC.1974.5408524>.

Infix functions in R are those that comes between its arguments such as %in%, +, and *. These are useful in R programming when manipulating data, performing logical operations, and making new functions. infixit extends the infix functions found in R to simplify frequent tasks, such as finding elements that are NOT in a set, in-line text concatenation, augmented assignment operations, additional logical and control flow operators, and identifying if a number or date lies between two others.

Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.

This package provides a variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.

Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martà n et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) <doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) <doi:10.1177/21582440211052556>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.

Compute onestep and multistep time series forecasts for machine learning models.

Generates the equiplot, an iconic dot-plot graph for visualizing inequalities, as well as three complex inequality measures: the slope index of inequality, the concentration index and the mean absolute difference to the mean. For more details see World Health Organization (2013) <https://www.who.int/docs/default-source/gho-documents/health-equity/handbook-on-health-inequality-monitoring/handbook-on-health-inequality-monitoring.pdf>.

This package provides 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.

This package provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the TWO-NN and Gride estimators and the Hidalgo Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the TWO-NN and Hidalgo models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).

This package provides methods for testing the equality of dependent intraclass correlation coefficients (ICCs) estimated using linear mixed-effects models. Several of the implemented approaches are based on the work of Donner and Zou (2002) <doi:10.1111/1467-9884.00324>.

Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.

Uses data and researcher's beliefs on measurement error and instrumental variable (IV) endogeneity to generate the space of consistent beliefs across measurement error, instrument endogeneity, and instrumental relevance for IV regressions. Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.

This package provides a suite of functions to use with regression models, including summaries, residual plots, and factor comparisons. Used as part of the Model Fitting module of iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.

Calculates calorific values (gross and net), density, relative density, and Wobbe indices together with their standard uncertainties from natural gas composition, implementing the method of ISO 6976:2016 "Natural Gas â Calculation of calorific values, density, relative density and Wobbe indices from composition". Uncertainty propagation follows Annex B of that standard. Reference: International Organization for Standardization (2016) <https://www.iso.org/standard/55842.html>.

Import and export data from the most common statistical formats by using R functions that guarantee the least loss of the data information, giving special attention to the date variables and the labelled ones.

Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too. This package uses the IP2Location.io API to query this data. To get started with a free API key, sign up here <https://www.ip2location.io/sign-up?ref=1>.

This package provides functions to estimate the probability to receive the observed treatment, based on individual characteristics. The inverse of these probabilities can be used as weights when estimating causal effects from observational data via marginal structural models. Both point treatment situations and longitudinal studies can be analysed. The same functions can be used to correct for informative censoring.

Convert between Irish grid references and Irish Grid coordinates. Irish grid references can also be converted to or from an sf object in any coordinate reference system. Precisions from 1 m to 100 km including 2 km (tetrads) are supported, as are datasets with mixed precision. Conversion to sf polygons is precision-aware.

Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers. The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal dot format, e.g., 3E7.1CBE991A14. These are referred to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary structure (see <https://en.wikipedia.org/wiki/ISO_11784_and_ISO_11785>). There are two flavours of this format, a left and a right variation. Which flavour a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically.