This package provides the conditional Nelson-Aalen and Aalen-Johansen estimators. The methods are based on Bladt & Furrer (2023), in preparation.

Adjusts output of cranlogs package to account for CRAN'-wide daily automated downloads and re-downloads caused by package updates.

This package creates complex autoregressive distributed lag (ARDL) models and constructs the underlying unrestricted and restricted error correction model (ECM) automatically, just by providing the order. It also performs the bounds-test for cointegration as described in Pesaran et al. (2001) <doi:10.1002/jae.616> and provides the multipliers and the cointegrating equation. The validity and the accuracy of this package have been verified by successfully replicating the results of Pesaran et al. (2001) in Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.

This package provides tools for simulating data generated by direct observation recording. Behavior streams are simulated based on an alternating renewal process, given specified distributions of event durations and interim times. Different procedures for recording data can then be applied to the simulated behavior streams. Functions are provided for the following recording methods: continuous duration recording, event counting, momentary time sampling, partial interval recording, whole interval recording, and augmented interval recording.

This package provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, <doi:10.1111/ajps.12029>). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives.

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).

The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component.

This package provides basic functionalities to calculate the position of satellites given a known state vector. The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the asteRiskData package, available in a drat repository. To install this data package, run install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.org/NORAD/documentation/spacetrk.pdf>. David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>. Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>.

This package provides simple assertions with sensible defaults and customisable error messages. It offers convenient assertion call wrappers and a general assert function that can handle any condition. Default error messages are user friendly and easily customized with inline code evaluation and styling powered by the cli package.

Alternative and fast algorithms for the analysis of receiver operating characteristics curves (ROC curves) as described in Thomas et al. (2017) <doi:10.1186/s41512-017-0017-y> and Thomas et al. (2023) <doi:10.1016/j.ajogmf.2023.101110>.

An iterative process that optimizes a function by alternately performing restricted optimization over parameter subsets. Instead of joint optimization, it breaks the optimization problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.

This package provides a modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See Gadaleta et al (2016) <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.).

This package implements the Arellano-Bond estimation method combined with LASSO for dynamic linear panel models. See Chernozhukov et al. (2024) "Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models". arXiv preprint <doi:10.48550/arXiv.2402.00584>.

Client for AWS Comprehend <https://aws.amazon.com/comprehend>, a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction.

This package provides functions to compute various clinical scores used in healthcare. These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; the EPICES score, an individual indicator of precariousness considering its multidimensional nature; the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk; and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy. For detailed methodology, refer to Charlson et al. (1987) <doi:10.1016/0021-9681(87)90171-8> , Sass et al. (2006) <doi:10.1007/s10332-006-0131-5>, Kamath et al. (2001) <doi:10.1053/jhep.2001.22172>, Kim et al. (2008) <doi:10.1056/NEJMoa0801209> Kim et al. (2021) <doi:10.1053/j.gastro.2021.08.050>, Mungroop et al. (2019) <doi:10.1097/SLA.0000000000002620>, and de Pastena et al. (2023) <doi:10.1097/SLA.0000000000005497>..

Enables users of ArcGIS Enterprise', ArcGIS Online', or ArcGIS Platform to read, write, publish, or manage vector and raster data via ArcGIS location services REST API endpoints <https://developers.arcgis.com/rest/>.

Estimate group aggregates, where one can set user-defined conditions that each group of records must satisfy to be suitable for aggregation. If a group of records is not suitable, it is expanded using a collapsing scheme defined by the user. A paper on this package was published in the Journal of Statistical Software <doi:10.18637/jss.v112.i04>.

This package provides methods to construct frequentist confidence sets with valid marginal coverage for identifying the population-level argmin or argmax based on IID data. For instance, given an n by p loss matrixâ where n is the sample size and p is the number of modelsâ the CS.argmin() method produces a discrete confidence set that contains the model with the minimal (best) expected risk with desired probability. The argmin.HT() method helps check if a specific model should be included in such a confidence set. The main implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) "Winners with confidence: Discrete argmin inference with an application to model selection".

This package implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.

Supplies a set of functions to query air travel data for user- specified years and airports. Datasets include on-time flights, airlines, airports, planes, and weather.

R wrapper around the argon HTML library. More at <https://demos.creative-tim.com/argon-design-system/>.

Runs projections of groups of matrix projection models (MPMs), allowing density dependence mechanisms to work across MPMs. This package was developed to run both adaptive dynamics simulations such as pairwise and multiple invasibility analyses, and community projections in which species are represented by MPMs. All forms of MPMs are allowed, including integral projection models (IPMs). Also includes individual-based modeling (IBM) versions of these.

Calculate ActiGraph counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in ActiGraph Wearables" on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) and python implementation of this code (<https://github.com/actigraph/agcounts>).