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.

Calculate various information criteria in literature for "lm" and "glm" objects.

Prepare objects to implement models over spatial and spacetime domains with the INLA package (<https://www.r-inla.org>). These objects contain data to for the cgeneric interface in INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models proposed in Lindgren et. al. (2024) <https://raco.cat/index.php/SORT/article/view/428665>. Details are provided in the available vignettes and from the URL bellow.

Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.

This package provides a GUI designed to support the analysis of financial-economic time series data.

Convenient functions to create ggplot2 graphics following the editorial guidelines of the Institute for Applied Economic Research (Ipea).

This package implements the Information Combination (IComb) approach proposed by Nguyen, Vahid and Wickramasuriya (2025)<https://www.monash.edu/business/ebs/research/publications/ebs/2025/wp11-2025.pdf> for hierarchical forecast reconciliation. The method combines information from base forecasts constructed using different information sets while ensuring coherence. It is implemented using a penalized regression-based framework.

This package provides tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).

This package provides tools for calculating commerce metrics, pairing treatment and control results from A/B testing experiments, estimating incremental effects, and quantifying uncertainty with Student's t and nonparametric bootstrap confidence intervals. Includes validation helpers, a high-level analysis workflow, and compatibility functions for the original package interface.

Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package IDF provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].

Collect marketing data from Instagram Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on Koo and Pashley (2026) <doi:10.1093/biomet/asag013>, it includes block-level estimators and extends to unit-level effects using Horvitz-Thompson and Hájek estimators. The package also provides asymptotic confidence intervals to support valid statistical inference.

This is an Automatic Item Generator for Psychological Assessment. Items created with the IMak package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.

Fit unidimensional item response theory (IRT) models to test data, which includes both dichotomous and polytomous items, calibrate pretest item parameters, estimate examinees abilities, and examine the IRT model-data fit on item-level in different ways as well as provide useful functions related to IRT analyses such as differential item functioning analysis. In addition, the package provides a set of classical test theory functions for computing item- and test-level statistics (e.g., item difficulty, item-total correlation, and coefficient alpha) and for scoring and analyzing selected-response item data. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2020) <doi:10.1177/0146621620929431>). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>). The bisection() function was written by modifying the bisection() function (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve scoring in the est_score() function was written by modifying the irt.eq.tse() function (Gonzalez (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions of the catR package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).

This package provides a comprehensive toolkit for intraclass correlation coefficient (ICC) analysis, integrating three core functionalities: (1) Closed-form sample size calculation for ICC estimation with assurance probability, based on Zou (2012) <doi:10.1002/sim.5466>; (2) Full implementation of all 10 ICC types (6 common + 4 supplementary) for point estimation, exact confidence interval calculation, and formal hypothesis testing, following the methods of McGraw & Wong (1996) <doi:10.1037/1082-989X.1.1.30> and the standard decision framework; (3) An interactive shiny application that guides users through ICC type selection, performs calculations, and provides reliability evaluation based on the Koo & Li (2016) <doi:10.1016/j.jcm.2016.02.012> criteria. Compared to existing packages, it provides a unified decision workflow and supports all less common ICC variants.

Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) The its.analysis R package - Modelling short time series data <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.

This package contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.

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

Generates Rd files from R source code with comments. The main features of the default syntax are that (1) docs are defined in comments near the relevant code, (2) function argument names are not repeated in comments, and (3) examples are defined in R code, not comments. It is also easy to define a new syntax.

Non-parametric tests of independence (mutual or serial) between some quantitative random vectors, as described in Bilodeau M. and Lafaye de Micheaux P. (2009) <doi:10.1016/j.jspi.2008.11.006>, in Beran R., Bilodeau M. and Lafaye de Micheaux P. (2007) <doi:10.1016/j.jmva.2007.01.009> and in Fan Y., Lafaye de Micheaux P., Penev S. and Salopek D. (2017) <doi:10.1016/j.jmva.2016.09.014>.

Manipulate integer-bounded intervals including finding overlaps, piling and merging.

The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.

Index of Multiple Deprivation for UK nations at various geographical levels. In England, deprivation data is for Lower Layer Super Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities based on data from <https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019>. In Wales, deprivation data is for Lower Layer Super Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities based on data from <https://gov.wales/welsh-index-multiple-deprivation-full-index-update-ranks-2019>. In Scotland, deprivation data is for Data Zones, Intermediate Zones, and Council Areas based on data from <https://simd.scot>. In Northern Ireland, deprivation data is for Super Output Areas and Local Government Districts based on data from <https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-2017-nimdm2017>. The IMD package also provides the composite UK index developed by <https://github.com/mysociety/composite_uk_imd>.

Imputation of missing values using the last observation carried forward technique on Indonesia food prices data that is time series data. Also, this technique applies imputation to data whose dates do not appear directly. So that the series assumptions in the time series data are met.