Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.

R interface to access the web services of the ICES Stock Assessment Graphs database <https://sg.ices.dk>.

Calculation of key bacterial growth curve parameters using fourth degree polynomial functions. Six growth curve parameters are provided including peak growth rate, doubling time, lag time, maximum growth, and etc. ipolygrowth takes time series data from individual biological samples (with technical replicates) or multiple samples.

Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.

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

An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighted (IPW) estimation of average treatment effect (ATE) with misclassified binary outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals.

Implementation of the methodology proposed in Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

This package provides a collection of Irucka Embry's miscellaneous USGS functions (processing .exp and .psf files, statistical error functions, "+" dyadic operator for use with NA, creating ADAPS and QW spreadsheet files, calculating saturated enthalpy). Irucka created these functions while a Cherokee Nation Technology Solutions (CNTS) United States Geological Survey (USGS) Contractor and/or USGS employee.

This package provides functions to access data from public RESTful APIs including Nager.Date', World Bank API', and REST Countries API', retrieving real-time or historical data related to Indonesia, such as holidays, economic indicators, and international demographic and geopolitical indicators. The package also includes a curated collection of open datasets focused on Indonesia, covering topics such as consumer prices, poverty probability, food prices by region, tourism destinations, and minimum wage statistics. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and REST Countries API <https://restcountries.com/>.

This package provides a unified and consistent interface for computing classical and bootstrap confidence intervals for means, variances, proportions, variance ratios, and regression coefficients. The package offers a standardized output structure, S3 classes, and user-friendly methods to facilitate statistical analysis and reproducibility.

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are aHDL from Rosenbaum (2023a) <doi:10.1111/biom.13558> and bingeM from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data bingeM from binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, weightedRank and tightenBlock'.

This package produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>]. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).

Read data from LimeSurvey (<https://www.limesurvey.org/>) in a comfortable way. Heavily inspired by limer (<https://github.com/cloudyr/limer/>), which lacked a few comfort features for me.

This package provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class incidence2 is used to store computed incidence and can be easily manipulated, subsetted, and plotted.

Interface to India Meteorological Department (IMD) gridded daily rainfall (0.25 degree, 1901-present) and temperature (1.0 degree, 1951-present) binary data. Provides functions to download, read, extract by point or boundary, compute climate indices, perform trend analysis, and produce publication-quality maps with Survey of India approved boundaries.

Authentication can be the most difficult part about working with a new API. ibmAcousticR facilitates making a connection to the IBM Acoustic email campaign management API and executing various queries. The IBM Acoustic API documentation is available at <https://developer.ibm.com/customer-engagement/docs/>. This package is not supported by IBM'.

This package provides a set of tools to i) identify geographic areas with significant change over time in drug utilization, and ii) characterize common change over time patterns among the time series for multiple geographic areas. For reference, see below: 1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) <doi:10.1504/IJCBDD.2018.10011910> 2. Wu, S., Wu, H. (2013) <doi:10.1186/1471-2105-14-6> 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) <doi:10.1016/j.idm.2016.07.001>.

For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. Leaves are stacked vertically. At most twenty leaves are shown; For high frequency, each leaflet may represent more than one observation with multiplicity declared in the subtitle.

This package provides a dataset of the top colours of photos from Instagram taken in 2014 in the city of Vancouver, British Columbia, Canada. It consists of: top colour and counts data. This data was obtained using the Instagram API. Instagram is a web photo sharing service. It can be found at: <https://instagram.com>. The Instagram API is documented at: <https://instagram.com/developer/>.

The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) <doi:10.1198/016214506000000140>, convolution transformation methods proposed by Zeng and Zhu (2010) <doi:10.1016/j.jmva.2009.08.004>, and iterative Hessian transformation methods proposed by Cook and Li (2002) <doi:10.1214/aos/1021379861>. Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) <doi:10.5705/ss.202020.0312>, and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) <doi:10.1016/j.csda.2021.107380>.

Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. The method is described in detail in Carmo, Williams and Rast (2025) <https://osf.io/sh6ne>.

This package contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).

Pre-processing and basic analytical tasks for working with Eurostat's symmetric inputâ output tables, and basic inputâ output economics calculations. Part of rOpenGov <https://ropengov.github.io/> for open source open government initiatives.

Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.