Carries out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.

Vector operations between grapes: An infix-only package! The invctr functions perform common and less common operations on vectors, data frames matrices and list objects: - Extracting a value (range), or, finding the indices of a value (range). - Trimming, or padding a vector with a value of your choice. - Simple polynomial regression. - Set and membership operations. - General check & replace function for NAs, Inf and other values.

This package provides functions are provided to interpolate geo-referenced point data via Inverse Path Distance Weighting. Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances.

General purpose TIFF file I/O for R users. Currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from ImageJ and write TIFF files than can be correctly read by ImageJ <https://imagej.net/ij/>. Also supports text image I/O.

An R client for the iplookupapi.com IP Lookup API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://iplookupapi.com/docs> .

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 contains functions for the classification and ranking of top candidate features, reconstruction of networks from adjacency matrices and data frames, analysis of the topology of the network and calculation of centrality measures, and identification of the most influential nodes. Also, a function is provided for running SIRIR model, which is the combination of leave-one-out cross validation technique and the conventional SIR model, on a network to unsupervisedly rank the true influence of vertices. Additionally, some functions have been provided for the assessment of dependence and correlation of two network centrality measures as well as the conditional probability of deviation from their corresponding means in opposite direction. Fred Viole and David Nawrocki (2013, ISBN:1490523995). Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695. Adopted algorithms and sources are referenced in function document.

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/>.

Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) <doi:10.48550/arXiv.2409.07176> or Gu et al. (2023) <doi:10.1093/biomet/asad073>.

This package provides a set of utilities for manipulating index numbers series including chain-linking, re-referencing, and computing growth rates.

It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence, and functions for generating and analysing different types of dependence.

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

Implementation of the classifier described in the paper Ali HR et al (2014) <doi:10.1186/s13059-014-0431-1>. It uses copy number and/or expression form breast cancer data, trains a Tibshirani's pamr classifier with the features available and predicts the iC10 group.

This package provides functions to generate incidence matrices and bipartite graphs that have (1) a fixed fill rate, (2) given marginal sums, (3) marginal sums that follow given distributions, or (4) represent bill sponsorships in the US Congress <doi:10.31219/osf.io/ectms>. It can also generate an incidence matrix from an adjacency matrix, or bipartite graph from a unipartite graph, via a social process mirroring team, group, or organization formation <doi:10.48550/arXiv.2204.13670>, or examine the space of binary matrices with fixed marginals.

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>.

Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects means and residuals) is provided.

This package provides a systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. IDMIR first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs.

The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>. For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) <doi:10.18637/jss.v099.i09>.

Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) <doi:10.1126/science.aag1322>. Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc.

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 tools for easily and flexibly creating ggplot2 maps with inset maps. One crucial feature of maps is that they have fixed coordinate ratios, i.e., they cannot be distorted, which makes it difficult to manually place inset maps. This package provides functions to automatically position inset maps based on user-defined parameters, making it extremely easy to create maps with inset maps with minimal code.

An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning.

This package provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.

Up-to-date data from the Unicode CLDR Project (where CLDR stands for Common Locale Data Repository') are available here as a series of easy-to-parse datasets. Several functions are provided for extracting key elements from the tabular datasets.