Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using York', Titterington', Ludwig and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.

This package implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.

This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participantâ s reaction time (in milliseconds). Three sample datasets are included in this package (labeled IAT', TooFastIAT', and BriefIAT') to practice with.

Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.

Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations. See Fox et al. (2024) <doi:10.1016/j.crmeth.2024.100884> for details.

An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. â s digitize package (2011) <doi:10.32614/RJ-2011-004> . For more complex and tangled together graphs, digitizing software, such as DigitizeIt (for MAC or windows) or ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) <doi:10.1186/1471-2288-12-9> with some modifications.

This package provides functions to compute and plot Krippendorff's inter-coder reliability coefficient alpha and bootstrapped uncertainty estimates (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to make use of parallel threads where supported.

This package provides a set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1017/pan.2021.19>.

Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or modelled using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.

An implementation of the initial guided analytics for parameter testing and controlband extraction framework. Functions are available for continuous and categorical target variables as well as for generating standardized reports of the conducted analysis. See <https://github.com/stefan-stein/igate> for more information on the technology.

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

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).

We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.

This package provides functions to read, process and analyse accelerometer data related to mechanical loading variables. This package is developed and tested for use with raw accelerometer data from triaxial ActiGraph <https://theactigraph.com> accelerometers.

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

Perform fast and memory efficient time-weighted averaging of values measured over intervals into new arbitrary intervals. This package is useful in the context of data measured or represented as constant values over intervals on a one-dimensional discrete axis (e.g. time-integrated averages of a curve over defined periods). This package was written specifically to deal with air pollution data recorded or predicted as averages over sampling periods. Data in this format often needs to be shifted to non-aligned periods or averaged up to periods of longer duration (e.g. averaging data measured over sequential non-overlapping periods to calendar years).

Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

R interface to access the web services of the ICES Stock Database <https://sd.ices.dk>.

Simulate inventory policies with and without forecasting, facilitate inventory analysis calculations such as stock levels and re-order points,pricing and promotions calculations. The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations. The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization. The functions are referenced from : 1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. 2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. 3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. 4-Ballou, R.H. Business Logistics Management. 5-MIT Micromasters Program. 6- Columbia University course for supply and demand analysis. 8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W For further details or correspondence :<www.linkedin.com/in/haythamomar>, <www.rescaleanalytics.com>.

Select set of parametric and non-parametric statistical tests. inferr builds upon the solid set of statistical tests provided in stats package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.

Cluster sampling is a valuable approach when constructing a comprehensive list of individual units is challenging. It provides operational and cost advantages. This package is designed to test the efficiency of cluster sampling in terms cluster variance and design effect in context to crop surveys. This package has been developed using the algorithm of Iqbal et al. (2018) <doi:10.19080/BBOAJ.2018.05.555673>.

Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the SparkR package (<https://spark.apache.org/docs/latest/sparkr.html>). The optional ggplot2 package is needed for the plot.idaLm() function only.

Call wrappers for Istanbul Metropolitan Municipality's Open Data Portal (Turkish: İstanbul BüyükŠehir Belediyesi Açık Veri Portalı) at <https://data.ibb.gov.tr/en/>.

Generates a Graphviz graph of the most significant 3-way interaction gains (i.e. conditional information gains) based on a provided discrete data frame. Various output formats are supported ('Graphviz', SVG, PNG, PDF, PS). For references, see the webpage of Aleks Jakulin <http://stat.columbia.edu/~jakulin/Int/>.