This package provides tools for the design of prospective studies using Personalised Synthetic Controls. Can be used in either single arm or randomised studies.

Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: data', a dataframe containing player data for any sports; player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; name_col', specifying the name of the column in the dataframe that contains the player names; and player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.

This package contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design.

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.

This package provides tools for exploring projection pursuit classification tree using various projection pursuit indexes.

This package provides a unified interface to access and manipulate various Philippine statistical classifications. It allows users to retrieve, filter, and harmonize classification data, making it easier to work with Philippine statistical data in R.

This package provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.

Automates the process of creating a scale bar and north arrow in any package that uses base graphics to plot in R. Bounding box tools help find and manipulate extents. Finally, there is a function to automate the process of setting margins, plotting the map, scale bar, and north arrow, and resetting graphic parameters upon completion.

This package provides a nonparametric, multicore-capable plausible naive Bayes classifier based on the Pareto density estimation (PDE) featuring a plausible approach to a pitfall in the Bayesian theorem covering low evidence cases. Stier, Q., Hoffmann, J., and Thrun, M.C.: "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" (2026), Machine Learning and Knowledge Extraction (MAKE), <DOI:10.3390/make8010013>.

ProTracker is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play ProTracker module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of ProTracker and the Commodore Amiga alive. This package is the successor of ProTrackR providing better performance.

This package provides functions and example datasets for phytosociological analysis, forest inventory, biomass and carbon estimation, and visualization of vegetation data. Includes functions to compute structural parameters [phytoparam(), summary.param(), stats()], estimate above-ground biomass and carbon [AGB()], stratify wood volume by diameter at breast height (DBH) classes [stratvol()], generate collector and rarefaction curves [collector.curve(), rarefaction()], and visualize basal areas on quadrat maps [BAplot(), including rectangular plots and individual coordinates]. Several example datasets are provided to demonstrate the functionality of these tools. For more details see FAO (1981, ISBN:92-5-101132-X) "Manual of forest inventory", IBGE (2012, ISBN:9788524042720) "Manual técnico da vegetação brasileira" and Heringer et al. (2020) "Phytosociology in R: A routine to estimate phytosociological parameters" <doi:10.22533/at.ed.3552009033>.

This package implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <arXiv:2303.04754>) and has been found to outperform several other commonly applied estimators.

This package provides a collection of software provides R support for ADMB (Automatic Differentiation Model Builder) and a GUI interface facilitates the conversion of ADMB template code to C code followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a GUI'.

Allows users to access the Oregon State Prism climate data (<https://prism.nacse.org/>). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.

Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the General Framework for Regression with Mismatched Data developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).

This package provides a collection of utilities and ggplot2 extensions to assist with visualisations in genomic epidemiology. This includes the phylepic chart, a visual combination of a phylogenetic tree and a matched epidemic curve. The included ggplot2 extensions such as date axes binned by week are relevant for other applications in epidemiology and beyond. The approach is described in Suster et al. (2024) <doi:10.1101/2024.04.02.24305229>.

This package provides an interface to access public economic and financial data for economic research and quantitative analysis. The data sources including NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative functions for trading strategies based on the data.table', TTR', PerformanceAnalytics and etc packages.

This package provides functions to create high-quality, publication-ready plots for numeric and categorical data, including bar plots, violin plots, boxplots, line plots, error bars, correlation plots, linear model plots, odds ratio plots, and normality plots.

This package provides methods to detect genetic markers involved in biological adaptation. pcadapt provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) <DOI:10.1111/1755-0998.12592> and later revised in (Privé, 2020) <DOI:10.1093/molbev/msaa053>.

Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...

Returns almost all features that has been extracted from Position Specific Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) and 20 columns produced by PSI-BLAST which is a program to produce PSSM Matrix from multiple sequence alignment of proteins see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some of these features are described in Zahiri, J., et al.(2013) <DOI:10.1016/j.ygeno.2013.05.006>, Saini, H., et al.(2016) <DOI:10.17706/jsw.11.8.756-767>, Ding, S., et al.(2014) <DOI:10.1016/j.biochi.2013.09.013>, Cheng, C.W., et al.(2008) <DOI:10.1186/1471-2105-9-S12-S6>, Juan, E.Y., et al.(2009) <DOI:10.1109/CISIS.2009.194>.

Pivot easily by specifying rows, columns, values and split.

This package provides a direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.

Algorithms and subroutines for patient-reported outcome data analysis.