This package provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data. Linear models with hyperbolic errors may be fitted using hyperblmFit.

The Project Revenue Tryton module computes revenue and cost per task and project. The revenue uses the list price of the product. If the product's unit of measure is time based, the revenue is computed as the product of the price and the hours of effort otherwise the price is considered as fixed. The cost is computed by summing the cost of all the linked time sheets and the linked purchase lines.

This package provides a refactoring tool based on the Emacs Semantic parser framework. For C and C++ it supports operations such as:

1. Generating class implementations

2. Generating function prototypes

3. Converting functions to function pointers

4. Moving semantic units

5. etc...

For Lisp dialects like Clojure, ELisp, and Scheme, it supports operations such as:

1. Formatting the whole buffer

2. Converting sexpressions to one or multiple lines

3. etc...

This package provides utilities for implementing and composing tracing subscribers.

Tracing is a framework for instrumenting Rust programs to collect scoped, structured, and async-aware diagnostics. The Subscriber trait represents the functionality necessary to collect this trace data. This crate contains tools for composing subscribers out of smaller units of behaviour, and batteries-included implementations of common subscriber functionality.

Tracing-subscriber is intended for use by both Subscriber authors and application authors using tracing to instrument their applications.

This package provides utilities for implementing and composing tracing subscribers.

Tracing is a framework for instrumenting Rust programs to collect scoped, structured, and async-aware diagnostics. The Subscriber trait represents the functionality necessary to collect this trace data. This crate contains tools for composing subscribers out of smaller units of behaviour, and batteries-included implementations of common subscriber functionality.

Tracing-subscriber is intended for use by both Subscriber authors and application authors using tracing to instrument their applications.

This module reads a file backwards line by line. It is simple to use, memory efficient and fast. It supports both an object and a tied handle interface.

It is intended for processing log and other similar text files which typically have their newest entries appended to them. By default files are assumed to be plain text and have a line ending appropriate to the OS. But you can set the input record separator string on a per file basis.

It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate conformalInference package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>, Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

Production efficiency and economic efficiency are crucial concepts in agriculture/horticulture for sustainable and profitable practices. It helps to determine the optimal use of resources to maximize outputs and profitability. Production efficiency focuses on the optimal use of resources to produce goods, while economic efficiency ensures these goods are produced and allocated in a way that maximizes economic welfare. Production efficiency and economic efficiency are calculated with the help of the formula given by (Kumar et al., 2017) <doi:10.21921/jas.v4i04.10202>.

This package provides a Python package of Roman Datamodels for the calibration pipelines started with the JWST calibration pipelines. The goal for the JWST pipelines was motivated primarily by the need to support FITS data files, specifically with isolating the details of where metadata and data were located in the FITS file from the representation of the same items within the Python code. That is not a concern for Roman since FITS format data files will not be used by the Roman calibration pipelines.

Estimate and return the needed parameters for visualizations designed for OpenBudgets.eu <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the OpenBudgets.eu data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to JSON format and use them as input in a different graphical interface.

Includes functions for calculating basic indices of macrozoobenthos for water quality and is designed to provide researchers and environmental professionals with a comprehensive tool for evaluating the ecological health of aquatic ecosystems.The package is based on the following references: Paisley, M. F., Trigg, D. J. and Walley, W. J. (2014)<doi:10.1002/rra.2686>. Arslan, N., Salur, A., Kalyoncu, H. et al.(2016) <doi:10.1515/biolog-2016-0005>. Hilsenhoff W.L. (1987). Hilsenhoff. W.L. (1988) Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B. (1999).

This permutation based hypothesis test, suited for several types of data supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance, several centrality measures, etc.). Network structures are estimated with l1-regularization. The Network Comparison Test is suited for comparison of independent (e.g., two different groups) and dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021), available from <doi:10.1037/met0000476>.

Unified interface for the estimation of causal networks, including the methods backShift (from package backShift'), bivariateANM (bivariate additive noise model), bivariateCAM (bivariate causal additive model), CAM (causal additive model) (from package CAM'; the package is temporarily unavailable on the CRAN repository; formerly available versions can be obtained from the archive), hiddenICP (invariant causal prediction with hidden variables), ICP (invariant causal prediction) (from package InvariantCausalPrediction'), GES (greedy equivalence search), GIES (greedy interventional equivalence search), LINGAM', PC (PC Algorithm), FCI (fast causal inference), RFCI (really fast causal inference) (all from package pcalg') and regression.

This module is intended to provide a cryptographically-secure replacement for Perl's built-in rand function. "Cryptographically secure", in this case, means:

1. No matter how many numbers you see generated by the random number generator, you cannot guess the future numbers, and you cannot guess the seed.

2. There are so many possible seeds that it would take decades, centuries, or millennia for an attacker to try them all.

3. The seed comes from a source that generates relatively strong random data on your platform, so the seed itself will be as random as possible.

The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.

Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate the difference between two binomial proportions. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of binomial observations are provided. In all cases, estimation of the difference between two binomial proportions is considered. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L., du Berger R. and Bélisle P. (1997) <doi:10.1002/(sici)1097-0258(19970415)16:7%3C769::aid-sim495%3E3.0.co;2-v>.

An infra-structure for estimating the predictive performance of predictive models. In this context, it can also be used to compare and/or select among different alternative ways of solving one or more predictive tasks. The main goal of the package is to provide a generic infra-structure to estimate the values of different metrics of predictive performance using different estimation procedures. These estimation tasks can be applied to any solutions (workflows) to the predictive tasks. The package provides easy to use standard workflows that allow the usage of any available R modeling algorithm together with some pre-defined data pre-processing steps and also prediction post- processing methods. It also provides means for addressing issues related with the statistical significance of the observed differences.

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu, X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.

This package provides a function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>. More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, <doi:10.1007/978-1-0716-1839-4_12>.

Path::Iterator::Rule iterates over files and directories to identify ones matching a user-defined set of rules. The API is based heavily on File::Find::Rule, but with more explicit distinction between matching rules and options that influence how directories are searched. A Path::Iterator::Rule object is a collection of rules (match criteria) with methods to add additional criteria. Options that control directory traversal are given as arguments to the method that generates an iterator.

A summary of features for comparison to other file finding modules:

• provides many helper methods for specifying rules

• offers (lazy) iterator and flattened list interfaces

• custom rules implemented with callbacks

• breadth-first (default) or pre- or post-order depth-first searching

• follows symlinks (by default, but can be disabled)

• directories visited only once (no infinite loop; can be disabled)

• doesn't chdir during operation

• provides an API for extensions

As a convenience, the PIR module is an empty subclass of this one that is less arduous to type for one-liners.

Tri-hierarchical incomplete block design is defined as an arrangement of v treatments each replicated r times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) <doi:10.1093/biomet/54.3-4.479>. This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions.

Google Trends provides cross-sectional and time-series data on searches, but lacks readily available longitudinal data. Researchers, who want to create longitudinal Google Trends on their own, face practical challenges, such as normalized counts that make it difficult to combine cross-sectional and time-series data and limitations in data formats and timelines that limit data granularity over extended time periods. This package addresses these issues and enables researchers to generate longitudinal Google Trends data. This package is built on pytrends', a Python library that acts as the unofficial Google Trends API to collect Google Trends data. As long as the Google Trends API', pytrends and all their dependencies are working, this package will work. During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of Google Trends data, once the data is created, this package coverts it to longitudinal data. In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect Google Trends data. This error indicates that the user has exceeded the rate limits set by the Google Trends API'. For more information about the Google Trends API - pytrends', visit <https://pypi.org/project/pytrends/>.

In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from AllMetrics package. â input_dfâ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular modelâ s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as â MINâ and other values are denoted as â NAâ . Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as â MAXâ and other values are denoted as â NAâ . â output_dfâ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of â input_dfâ . Values in â output_dfâ are corresponding â NAâ , â MINâ or â MAXâ . Finally, the column containing minimum number of â NAâ values is denoted as the best column. â min_NA_colâ gives the name of the best column (model). â min_NA_valuesâ are the corresponding metrics values. âBestColumn_metricsâ is the data frame (dimension: 1*8) containing different metrics of the best column (model). â best_column_resultsâ is the final result (a list) containing all of these output elements. In special case, if two columns having equal NA', it will be checked among these two column which one is having least NA in first five rows and will be inferred as the best. More details about AllMetrics can be found in Garai (2023) <doi:10.13140/RG.2.2.18688.30723>.

Rcmdr Plugin for the FactoMineR package.