Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.

Power and Sample Size for Health Researchers is a Shiny application that brings together a series of functions related to sample size and power calculations for common analysis in the healthcare field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, regression coefficients (linear, logistic, gamma, and Cox), linear mixed model, Cronbach's alpha, interobserver agreement, intraclass correlation coefficients, limit of agreement on Bland-Altman plots, area under the curve, sensitivity and specificity incorporating the prevalence of disease. You can also use the online version at <https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/>.

This package provides a comprehensive set of tools for describing and visualizing panel data structures, as well as for summarizing and visualizing variables within a panel data context.

Prediction limits for the Poisson distribution are produced from both frequentist and Bayesian viewpoints. Limiting results are provided in a Bayesian setting with uniform, Jeffreys and gamma as prior distributions. More details on the methodology are discussed in Bejleri and Nandram (2018) <doi:10.1080/03610926.2017.1373814> and Bejleri, Sartore and Nandram (2021) <doi:10.1007/s42952-021-00157-x>.

Automated backtesting of multiple portfolios over multiple datasets of stock prices in a rolling-window fashion. Intended for researchers and practitioners to backtest a set of different portfolios, as well as by a course instructor to assess the students in their portfolio design in a fully automated and convenient manner, with results conveniently formatted in tables and plots. Each portfolio design is easily defined as a function that takes as input a window of the stock prices and outputs the portfolio weights. Multiple portfolios can be easily specified as a list of functions or as files in a folder. Multiple datasets can be conveniently extracted randomly from different markets, different time periods, and different subsets of the stock universe. The results can be later assessed and ranked with tables based on a number of performance criteria (e.g., expected return, volatility, Sharpe ratio, drawdown, turnover rate, return on investment, computational time, etc.), as well as plotted in a number of ways with nice barplots and boxplots.

Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the arules package by Hahsler et al. (2025)<doi:10.32614/CRAN.package.arules> and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science.

Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.

Makes it easy to build panel data in wide format from Panel Survey of Income Dynamics (PSID) delivered raw data. Downloads data directly from the PSID server using the SAScii package. psidR takes care of merging data from each wave onto a cross-period index file, so that individuals can be followed over time. The user must specify which years they are interested in, and the PSID variable names (e.g. ER21003) for each year (they differ in each year). The package offers helper functions to retrieve variable names from different waves. There are different panel data designs and sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about the PSID can be obtained at <https://simba.isr.umich.edu/data/data.aspx>.

Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.

Constructors of waveband objects for commonly used biological spectral weighting functions (BSWFs) and for different wavebands describing named ranges of wavelengths in the ultraviolet (UV), visible (VIS) and infrared (IR) regions of the electromagnetic spectrum. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

This package provides a variety of tools relevant to the analysis of marine soundscape data. There are tools for downloading AIS (automatic identification system) data from Marine Cadastre <https://hub.marinecadastre.gov>, connecting AIS data to GPS coordinates, plotting summaries of various soundscape measurements, and downloading relevant environmental variables (wind, swell height) from the National Center for Atmospheric Research data server <https://gdex.ucar.edu/datasets/d084001/>. Most tools were developed to work well with output from Triton software, but can be adapted to work with any similar measurements.

This package provides classes to pre-process microarray gene expression data as part of the OOMPA collection of packages described at <http://oompa.r-forge.r-project.org/>.

This package provides tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's Motive', the Straw Lab's Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli.

This package provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.

This package implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.

Calculate (stratified) percentiles on a data.frame Stratification will split the data.frame into subgroups and calculate percentiles for each independently.

The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), including both densities and overlaying mixture weight component curves from the fit GMM. The package also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying the "fun" argument within "stat_function" in a ggplot2 object.

Directly pipes raw quantitative PCR (qPCR) machine outputs into downstream analyses using the comparative Ct (Delta-Delta Ct) method described by Livak and Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Streamlines the workflow from Excel export to publication-ready plots. Integrates unique visual quality control by reconstructing 96-well plate heatmaps, allowing users to instantly detect pipetting errors, edge effects, and outliers. Key features include automated error propagation, laboratory master mix calculations, and generation of bar charts and volcano plots.

An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is extended to allow inbred founders. pedprobr is part of the pedsuite', a collection of packages for pedigree analysis in R. In particular, pedprobr depends on pedtools for pedigree manipulations and pedmut for mutation modelling. For more information, see Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302).

Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations. The package impose regulatory constrains and sanity checking for common bioanalytical procedures. Additionally, PKbioanalysis provides a relational infrastructure for plate management and injection sequence.

This package creates a data frame with the residuals of partial regressions of the main explanatory variable and the variable of interest. This method follows the Frisch-Waugh-Lovell theorem, as explained in Lovell (2008) <doi:10.3200/JECE.39.1.88-91>.

Makes the time series prediction easier by automatizing this process using four main functions: prep(), modl(), pred() and postp(). Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret).

Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).

Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research.