Pull data from the Impect Customer API <https://glossary.impect.com/api-design>. The package can retrieve data such as events or match sums.

Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) <http://rcea.org/RePEc/pdf/wp23-11.pdf> . The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.

Reproducible, programmatic retrieval of datasets from the Inter-university Consortium for Political and Social Research archive.

This package provides a method that estimates an IV-optimal individualized treatment rule. An individualized treatment rule is said to be IV-optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. Please refer to <arXiv:2002.02579> for more details on the methodology and some theory underpinning the method. Function IV-PILE() uses functions in the package locClass'. Package locClass can be accessed and installed from the R-Forge repository via the following link: <https://r-forge.r-project.org/projects/locclass/>. Alternatively, one can install the package by entering the following in R: install.packages("locClass", repos="<http://R-Forge.R-project.org>")'.

For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.

Assists in generating categorical clustered outcome data, estimating the Intracluster Correlation Coefficient (ICC) for nominal or ordinal data with 2+ categories under the resampling and method of moments (MoM) methods, with confidence intervals.

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.

Suite of functions that help simulate elections under different electoral systems, which are then used to compute incentives generated by these systems in terms of the inter- and intra-party dimensions of electoral competition.

This package provides functions to parse strings with ISO8601 dates, times, and date-times into R-objects. Additionally, there are functions to determine the type of ISO8601 string and to standardise ISO8601 strings.

This function predicts item response probabilities and item responses using the item-focused tree model. The item-focused tree model combines logistic regression with recursive partitioning to detect Differential Item Functioning in dichotomous items. The model applies partitioning rules to the data, splitting it into homogeneous subgroups, and uses logistic regression within each subgroup to explain the data. Differential Item Functioning detection is achieved by examining potential group differences in item response patterns. This method is useful for understanding how different predictors, such as demographic or psychological factors, influence item responses across subgroups.

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.

This package implements the Interpolate, Truncate, Project (ITP) root-finding algorithm developed by Oliveira and Takahashi (2021) <doi:10.1145/3423597>. The user provides the function, from the real numbers to the real numbers, and an interval with the property that the values of the function at its endpoints have different signs. If the function is continuous over this interval then the ITP method estimates the value at which the function is equal to zero. If the function is discontinuous then a point of discontinuity at which the function changes sign may be found. The function can be supplied using either an R function or an external pointer to a C++ function. Tuning parameters of the ITP algorithm can be set by the user. Default values are set based on arguments in Oliveira and Takahashi (2021).

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.

Clustering algorithm developed for use with plot inventories of species. It groups plots by subsets of diagnostic species rather than overall species composition. There is an unsupervised and a supervised mode, the latter accepting suggestions for species with greater weight and cluster medoids.

This package provides efficient implementation of the Isolate-Detect methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the "deterministic + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>. Currently implemented scenarios are: piecewise-constant signal with Gaussian noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.

Improved methods based on inverse probability weighting and outcome regression for causal inference and missing data problems.

This package provides a set of functions for the modeling of data derived from the Minidisc Infiltrometer device. It calculates cumulative infiltration and square root of time. Also, it calculates the A parameter based on soil physical properties.

An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). The algorithm is explained at <doi:10.5201/ipol.2016.175>.

Chi-square tests are computed with corrections.

This program facilitates exporting igraph graphs to the SoNIA file format.

This package provides a toolbox for constructing potential landscapes for Ising networks. The parameters of the networks can be directly supplied by users or estimated by the IsingFit package by van Borkulo and Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from empirical data. The Ising model's Boltzmann distribution is preserved for the potential landscape function. The landscape functions can be used for quantifying and visualizing the stability of network states, as well as visualizing the simulation process.

Various functions and a Shiny app to enrich the results of Multiple Correspondence Analysis with interpretive axes and planes (see Moschidis, Markos, and Thanopoulos, 2022; <doi:10.1108/ACI-07-2022-0191>).

This package provides a fragmentation spectra detection pipeline for high-throughput LC/HRMS data processing using peaklists generated by the IDSL.IPA workflow <doi:10.1021/acs.jproteome.2c00120>. The IDSL.CSA package can deconvolute fragmentation spectra from Composite Spectra Analysis (CSA), Data Dependent Acquisition (DDA) analysis, and various Data-Independent Acquisition (DIA) methods such as MS^E, All-Ion Fragmentation (AIF) and SWATH-MS analysis. The IDSL.CSA package was introduced in <doi:10.1021/acs.analchem.3c00376>.

This package provides a library for generic interval manipulations using a new interval vector class. Capabilities include: locating various kinds of relationships between two interval vectors, merging overlaps within a single interval vector, splitting an interval vector on its overlapping endpoints, and applying set theoretical operations on interval vectors. Many of the operations in this package were inspired by James Allen's interval algebra, Allen (1983) <doi:10.1145/182.358434>.