An implementation of the parameter cascade method in Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.

This R package allows the determination of some distributions of the voters power when passing laws in weighted voting situations.

This package provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.

In short, this package is a locator for cool, refreshing beverages. It will find and return the nearest location where you can get a cold one.

Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. Performs these linkage disequilibrium (LD) calculations on phased genotype data recorded using Genotype List (GL) String or columnar formats. Alternatively, generates expectation-maximization (EM) estimated haplotypes from phased data, or performs LD calculations on EM estimated haplotypes. Performs sign tests comparing LD values for phased and unphased datasets, and generates heat-maps for each LD measure. Described by Osoegawa et al. (2019a) <doi:10.1016/j.humimm.2019.01.010>, and Osoegawa et. al. (2019b) <doi:10.1016/j.humimm.2019.05.018>.

This package provides functions to aid in micro and macro economic analysis and handling of price and currency data. Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020).

Helps you determine the analysis window to use when analyzing densely-sampled time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007) <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints where significance of an effect begins and ends, and the results can be plotted in various types of heatmap for reporting. Mixed-effects models are supported using an implementation of the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.

Pool dilution is a isotope tracer technique wherein a biogeochemical pool is artifically enriched with its heavy isotopologue and the gross productive and consumptive fluxes of that pool are quantified by the change in pool size and isotopic composition over time. This package calculates gross production and consumption rates from closed-system isotopic pool dilution time series data. Pool size concentrations and heavy isotope (e.g., 15N) content are measured over time and the model optimizes production rate (P) and the first order rate constant (k) by minimizing error in the model-predicted total pool size, as well as the isotopic signature. The model optimizes rates by weighting information against the signal:noise ratio of concentration and heavy- isotope signatures using measurement precision as well as the magnitude of change over time. The calculations used here are based on von Fischer and Hedin (2002) <doi:10.1029/2001GB001448> with some modifications.

High-quality real-world data can be transformed into scientific real-world evidence for regulatory and healthcare decision-making using proven analytical methods and techniques. For example, propensity score (PS) methodology can be applied to select a subset of real-world data containing patients that are similar to those in the current clinical study in terms of baseline covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. Then, statistical methods such as the power prior approach or composite likelihood approach can be applied in each stratum to draw inference for the parameters of interest. This package provides functions that implement the PS-integrated real-world evidence analysis methods such as Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>, Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>.

This extension of the poems pattern-oriented modeling (POM) framework provides a collection of modules and functions customized for paleontological time-scales, and optimized for single-generation transitions and large populations, across multiple generations.

This package creates an interactive scatterplot matrix using the D3 JavaScript library. See <https://d3js.org/> for more information on D3.

Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.

Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot XVG output files from the GROMACS molecular dynamics package.

We extend dplyr and fuzzyjoin join functions with features to preprocess the data, apply various data checks, and deal with conflicting columns.

We propose a pair of summary measures for the predictive power of a prediction function based on a regression model. The regression model can be linear or nonlinear, parametric, semi-parametric, or nonparametric, and correctly specified or mis-specified. The first measure, R-squared, is an extension of the classical R-squared statistic for a linear model, quantifying the prediction function's ability to capture the variability of the response. The second measure, L-squared, quantifies the prediction function's bias for predicting the mean regression function. When used together, they give a complete summary of the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.

Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.

This package provides a partialised class that extends the partialising function of purrr by making it easier to change the arguments. This is similar to the function-like object in Julia (<https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects>).

Interactively explore various dependencies of a package(s) (on the Comprehensive R Archive Network Like repositories) and perform analysis using tidy philosophy. Most of the functions return a tibble object (enhancement of dataframe') which can be used for further analysis. The package offers functions to produce network and igraph dependency graphs. The plot method produces a static plot based on ggnetwork and plotd3 function produces an interactive D3 plot based on networkD3'.

Statistical functions to describe a Pareto Positive Stable (PPS) distribution and fit it to real data. Graphical and statistical tools to validate the fits are included.

This package provides functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.

The Penn World Table provides purchasing power parity and national income accounts converted to international prices for 189 countries for some or all of the years 1950-2010.

This package provides functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).

Pattern causality is a novel approach for detecting the hidden causality in the complex system.

This package provides a coding assistant using Perplexity's Large Language Models <https://www.perplexity.ai/> API. A set of functions and RStudio add-ins that aim to help R developers.