Turn numeric,data.frame,matrix into fraction form.

This package provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name feasts is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.

Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. The computations are based on the C++ library Armadillo through the R package Rcpp'.

Climate is a critical component limiting growing range of plant species, which also determines cultivar adaptation to a region. The evaluation of climate influence on fruit production is critical for decision-making in the design stage of orchards and vineyards and in the evaluation of the potential consequences of future climate. Bio- climatic indices and plant phenology are commonly used to describe the suitability of climate for growing quality fruit and to provide temporal and spatial information about regarding ongoing and future changes. fruclimadapt streamlines the assessment of climate adaptation and the identification of potential risks for grapevines and fruit trees. Procedures in the package allow to i) downscale daily meteorological variables to hourly values (Forster et al (2016) <doi:10.5194/gmd-9-2315-2016>), ii) estimate chilling and forcing heat accumulation (Miranda et al (2019) <https://ec.europa.eu/eip/agriculture/sites/default/files/fg30_mp5_phenology_critical_temperatures.pdf>), iii) estimate plant phenology (Schwartz (2012) <doi:10.1007/978-94-007-6925-0>), iv) calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr et al (2017) <doi:10.3354/cr01532>), v) estimate the incidence of weather-related disorders in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi) estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)).

Offers tools for visualizing and analyzing size and power properties of tests for equal predictive accuracy, including Diebold-Mariano and related procedures. Provides multiple Diebold-Mariano test implementations based on fixed-smoothing approaches, including fixed-b methods such as Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) <doi:10.1002/jae.2756>, alongside conventional large-sample approximations. HAR inference involves nonparametric estimation of the long-run variance, and a key tuning parameter (the truncation parameter) trades off size and power. Lazarus, Lewis, and Stock (2021) <doi:10.3982/ECTA15404> theoretically characterize the size-power frontier for the Gaussian multivariate location model. ForeComp computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute finite-sample size and power, it fits a best approximating ARMA process to the input data and reports how the truncation parameter performs and how robust testing outcomes are to its choice.

This package contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported.

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the boot package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) <doi:10.1080/00031305.2020.1731599> for details.

Computes and plots prediction intervals for numerical data or prediction sets for categorical data using prior information. Empirical Bayes procedures to estimate the prior information from multi-group data are included. See, e.g.,Bersson and Hoff (2022) <arXiv:2204.08122> "Optimal Conformal Prediction for Small Areas".

This package provides a study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the median survival time) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. Though The survival endpoint is often encountered in phase II trials, the existing SSD methods cannot deal with the survival endpoint. Furthermore, the existing SSD wonâ t control the type I error rate. The proposed designs can â partiallyâ control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.

Handy functions and data to support the course book Empirical Research in Accounting: Tools and Methods (1st ed.). Chapman and Hall/CRC. <doi:10.1201/9781003456230> and <https://iangow.github.io/far_book/>.

Dataset of 302 measurements of 11 fish species to accompany the manuscript "Length-weight relationships of six freshwater fish species from lake Kirkkojarvi, Finland".

Finds the critical sample size ("critical point of stability") for a correlation to stabilize in Schoenbrodt and Perugini's definition of sequential stability (see <doi:10.1016/j.jrp.2013.05.009>).

Multifactor nonparametric analysis of variance based on ranks. Builds on the Kruskal-Wallis H test and its 2x2 Scheirer-Ray-Hare extension to handle any factorial designs. Provides effect sizes, Dunn-Bonferroni pairwise-comparison matrices, and simple-effects analyses. Tailored for psychology and the social sciences, with beginner-friendly R syntax and outputs that can be dropped into journal reports. Includes helpers to export tab-separated results and compact tables of descriptive statistics (to APA-style reports).

The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.

Allows the user to create a countdown in RMarkdown documents and shiny applications. The package is a wrapper of the JavaScript library flipdown.js'. See <https://pbutcher.uk/flipdown/> for more info.

Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.

Convenient classes to model fitness landscapes and fitness seascapes. A low-level package with which most users will not interact but upon which other packages modeling fitness landscapes and fitness seascapes will depend.

Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes. Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models. The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).

Function factories are functions that make functions. They can be confusing to construct. Straightforward techniques can produce functions that are fragile or hard to understand. While more robust techniques exist to construct function factories, those techniques can be confusing. This package is designed to make it easier to construct function factories.

Has two functions to help with calculating feature selection stability. Lump is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length. ASM is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection. The calculation for asm uses the Adjusted Stability Measure proposed in: Lustgarten', Gopalakrishnan', & Visweswaran (2009)<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815476/>.

Perform various floating catchment area methods to calculate a spatial accessibility index (SPAI) for demand point data. The distance matrix used for weighting is normalized in a preprocessing step using common functions (gaussian, gravity, exponential or logistic).

This package provides functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for viewing the details (size, time created, time modified, etc.) of a folder's top level contents.

Perform mathematical operations on R formula (add, subtract, multiply, etc.) and substitute parts of formula.

This package provides allele frequency data for Short Tandem Repeat human genetic markers commonly used in forensic genetics for human identification and kinship analysis. Includes published population frequency data from the US National Institute of Standards and Technology, Federal Bureau of Investigation and the UK government.