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].

This package provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling.

An EM algorithm to fit Mallows Models to full or partial rankings, with or without ties. Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.

Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi: 10.1080/03610926.2020.1764042>. The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split. This gain is aggregated for each split variable in a tree and averaged across trees.

This package provides a tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing) as described in Koohafkan MC, Younis BA (2015). "Open-channel computation with R." The R Journal, 7(2), 249â 262. <doi: 10.32614/RJ-2015-034>.

Provide simple mechanism to repeatedly evaluate an expression until either it succeeds or timeout exceeded. It is useful in situations that random failures could happen.

An implementation of calls designed to collect and organize Mastodon data via its Application Program Interfaces (API), which can be found at the following URL: <https://docs.joinmastodon.org/>.

This package implements robust median-based Bayesian growth curve models that handle Missing Completely at Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) missing-data mechanisms, and allow auxiliary variables. Models are fitted via rjags (interface to JAGS') and summarized with coda'.

Models and displays tephra transport through custom (windy, turbulent, heterogeneous) atmosphere over custom topography. Includes a Lagrangian (particle-tracking) tephra transport model and a function to save snapshots of model as png files.

This package provides functionality to group lines that form naturally continuous lines in a spatial network. The algorithm implemented is based on the Continuity in Street Networks (COINS) method from Tripathy et al. (2021) <doi:10.1177/2399808320967680>, which identifies continuous "strokes" in the network as the line strings that maximize the angles between consecutive segments.

Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of ruminate is to create a shiny interface to make these tools more broadly available while creating reproducible results.

This package provides a simple set of wrappers to easily use RDCOMClient for generating Microsoft PowerPoint presentations. Warning:this package is soon to be archived from CRAN.

Implementation of the Robust Gauss-Newton (RGN) algorithm, designed for solving optimization problems with a sum of least squares objective function. For algorithm details please refer to Qin et. al. (2018) <doi:10.1029/2017WR022488>.

Rcpp bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) <doi:10.1074/mcp.O114.037879>) for more details.

This package provides a checkbox group input for usage in a Shiny application. The checkbox group has a head checkbox allowing to check or uncheck all the checkboxes in the group. The checkboxes are customizable.

This package implements the network clustering algorithm described in Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, rSpectral then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This nodeâ s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity.

Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using the local polynomial density estimators: rddensity() to construct test statistics and p-values given a prespecified cutoff, rdbwdensity() to perform data-driven bandwidth selection, and rdplotdensity() to construct density plots.

Build regular expressions piece by piece using human readable code. This package is designed for interactive use. For package development, use the rebus.* dependencies.

Designed to create and display complex tables with R, the rtables R package allows cells in an rtables object to contain any high-dimensional data structure, which can then be displayed with cell-specific formatting instructions. Additionally, the rtables.officer package supports export formats related to the Microsoft Office software suite, including Microsoft Word ('docx') and Microsoft PowerPoint ('pptx').

TiddlyWiki is a unique non-linear notebook for capturing, organising and sharing complex information. rtiddlywiki is a R interface of TiddlyWiki <https://tiddlywiki.com> to create new tiddler from R Markdown file, and then put into a local TiddlyWiki server if it is available.

This package provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon-related timescales (cal BP, cal BC/AD, C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package uses the calibration curves from the data package rintcal'.

Read the data from Origin(R) project files ('*.opj') <https://www.originlab.com/doc/User-Guide/Origin-File-Types>. No write support is planned.

Simple, native RethinkDB client.

Routines that allow the user to run a large number of goodness-of-fit tests. It allows for data to be continuous or discrete. It includes routines to estimate the power of the tests and display them as a power graph. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones.