This package performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <DOI:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) <https://www.jstor.org/stable/2242558?seq=1>.

This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).

This package provides a series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.

Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

Rcmdr menu support for many of the functions in the HH package. The focus is on menu items for functions we use in our introductory courses.

The goal of Rigma is to provide a user friendly client to the Figma API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a stable interface with the REST API. More than 20 methods are provided to interact with Figma files, and teams. Get design data into R by reading published components and styles, converting and downloading images, getting access to the full Figma file as a hierarchical data structure, and much more. Enhance your creativity and streamline the application development by automating the extraction, transformation, and loading of design data to your applications and HTML documents.

Statistical tools based on the probabilistic properties of the record occurrence in a sequence of independent and identically distributed continuous random variables. In particular, tools to prepare a time series as well as distribution-free trend and change-point tests and graphical tools to study the record occurrence. Details about the implemented tools can be found in Castillo-Mateo et al. (2023a) <doi:10.18637/jss.v106.i05> and Castillo-Mateo et al. (2023b) <doi:10.1016/j.atmosres.2023.106934>.

This package creates reports from Trello, a collaborative, project organization and list-making application. <https://trello.com/> Reports are created by comparing individual Trello board cards from two different points in time and documenting any changes made to the cards.

shiny extension that adds regular expression filtering capabilities to the choice vector of the select list.

REDUCE is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in LaTeX format. REDUCE is based on Lisp and is available on the two dialects Portable Standard Lisp ('PSL') and Codemist Standard Lisp ('CSL'). The redcas package provides an interface for executing arbitrary REDUCE code interactively from R', returning output as character vectors. R code and REDUCE code can be interspersed. It also provides a specialized function for calling the REDUCE feature for solving systems of equations, returning the output as an R object designed for the purpose. A further specialized function uses REDUCE features to generate LaTeX output and post-processes this for direct use in LaTeX documents, e.g. using Sweave'.

Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see, Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].

Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.

Many packages in the r-dcm family take similar arguments, which are checked for expected structures and values. Rather than duplicating code across several packages, commonly used check functions are included here. This package can then be imported to access the check functions in other packages.

Supports automated Markov chain Monte Carlo for arbitrarily structured correlation matrices. The user supplies data, a correlation matrix in symbolic form, the current state of the chain, a function that computes the log likelihood, and a list of prior distributions. The package's flagship function then carries out a parameter-at-a-time update of all correlation parameters, and returns the new state. The method is presented in Hughes (2023), in preparation.

This package provides a simple implementation of Binary Indexed Tree by R. The BinaryIndexedTree class supports construction of Binary Indexed Tree from a vector, update of a value in the vector and query for the sum of a interval of the vector.

Gather boxscore, play-by-play, and auxiliary data from Major League Volleyball (MLV) <https://provolleyball.com>, League One Volleyball Pro (LOVB) <https://www.lovb.com/pro-league>, and Athletes Unlimited Pro Volleyball (AU) <https://auprosports.com/volleyball/> to create a repository of basic and advanced statistics for teams and players.

Interface to the ReebGraphPairing program to compute critical points of Reeb graphs following Tu, Hajij, & Rosen (2019) <doi:10.1007/978-3-030-33720-9_8> via the rJava package. Also store Reeb graphs in a minimal S3 class, convert between other network data structures, and post-process pairing data to obtain extended persistent homology following Carrière & Oudot (2018) <doi:10.1007/s10208-017-9370-z>.

This package provides a machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.

Make optimal decisions for your personal or household finances. Use tools and methods that are selected carefully to align with academic consensus, bridging the gap between theoretical knowledge and practical application. They help you find your own personalized optimal discretionary spending or optimal asset allocation, and prepare you for retirement or financial independence. The optimal solution to this problems is extremely complex, and we only have a single lifetime to get it right. Fortunately, we now have the user-friendly tools implemented, that integrate life-cycle models with single-period net-worth mean-variance optimization models. Those tools can be used by anyone who wants to see what highly-personalized optimal decisions can look like. For more details see: Idzorek T., Kaplan P. (2024, ISBN:9781952927379), Haghani V., White J. (2023, ISBN:9781119747918).

This package provides a translation layer between R and CDO operators. Each operator is it's own function with documentation. Nested or piped functions will be translated into CDO chains.

R^2 measure of explained variation under the semiparametric additive hazards model is estimated. The measure can be used as a measure of predictive capability and therefore it can be adopted in model selection process. Rava, D. and Xu, R. (2020) <arXiv:2003.09460>.

Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to extract information at the level of gene, mRNA or protein accessions.

Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831>.

Aims at loading Google Adwords data into R. Adwords is an online advertising service that enables advertisers to display advertising copy to web users (see <https://developers.google.com/adwords/> for more information). Therefore the package implements three main features. First, the package provides an authentication process for R with the Google Adwords API (see <https://developers.google.com/adwords/api/> for more information) via OAUTH2. Second, the package offers an interface to apply the Adwords query language in R and query the Adwords API with ad-hoc reports. Third, the received data are transformed into suitable data formats for further data processing and data analysis.