The merits of TIMESAT and phenopix are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And optimx is used to select best optimization method for different curve fitting methods. Reference: Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>; Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. <doi:10.1029/2020JG005636>; Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13â 24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982â 2013). Agric. For. Meteorol. 248, 408â 417. <doi:10.1016/j.agrformet.2017.10.026>.

This package provides functions to automatically build a directory structure for a new R project. Using this structure, ProjectTemplate automates data loading, preprocessing, library importing and unit testing.

Interface to the Pharmpy pharmacometrics library. The Reticulate package is used to interface Python from R.

Makes it easy to push data to Power BI using R and the Power BI REST APIs (see <https://docs.microsoft.com/en-us/rest/api/power-bi/>). A set of functions for turning data frames into Power BI datasets and refreshing these datasets are provided. Administrative tasks such as monitoring refresh statuses and pulling metadata about workspaces and users are also supported.

Programmatic access to microdata from Colombia's Gran Encuesta Integrada de Hogares ('GEIH'), published by DANE'. Provides a tidy interface to download, parse, and harmonize labor market surveys from 2007 to present. R companion to the pulso-co Python package.

Handles and formats author information in scientific writing in R Markdown and Quarto'. plume provides easy-to-use and flexible tools for inserting author data in YAML as well as generating author and contribution lists (among others) as strings from tabular data.

Annotate plots with legends for continuous variables and colour spectra using the base graphics plotting tools; and manipulate irregular polygons. Includes palettes for colour-blind viewers.

Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository.

Automated pain scoring from paw withdrawal tracking data. Based on Jones et al. (2020) "A machine-vision approach for automated pain measurement at millisecond timescales" <doi:10.7554/eLife.57258>.

Normalizes city names for EEA countries and matches them to NUTS 3 regions using provided crosswalks. Features include comprehensive normalization rules, cascading matching logic (Exact NUTS -> Exact LAU -> Fuzzy), and single-source data synthesis. The package implements the NUTS classification as described in the NUTS methodology (Eurostat (2021) <https://ec.europa.eu/eurostat/web/nuts>).

It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) <doi:10.1093/biomet/ass032>. The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using rpart and ctree functions), two matching methods (using Matching and MatchIt packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results.

This package provides a tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.

This package provides functions for quantifying visible (VIS) and ultraviolet (UV) radiation in relation to the photoreceptors Phytochromes, Cryptochromes, and UVR8 which are present in plants. It also includes data sets on the optical properties of plants. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Computation of robust standard errors of Poisson fixed effects models, following Wooldridge (1999).

This package provides tools for the evaluation of interim analysis plans for sequentially monitored trials on a survival endpoint; tools to construct efficacy and futility boundaries, for deriving power of a sequential design at a specified alternative, template for evaluating the performance of candidate plans at a set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.

This package provides a toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>. Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.

Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.

Given an arbitrary set of spatial regions and road networks, generate a set of representative points, or pseudohouseholds, that can be used for travel burden analysis. Parallel processing is supported.

Calculation of the parametric, nonparametric confidence intervals for the difference or ratio of location parameters, nonparametric confidence interval for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial proportions for comparison of independent samples. Common wrapper functions to split data sets and apply confidence intervals or tests to these subsets. A by-statement allows calculation of CI separately for the levels of further factors. CI are not adjusted for multiplicity.

This package provides a comprehensive framework for model fitting and simulation of drug release kinetics, pharmacokinetics (PK), and pharmacodynamics (PD). The package implements widely used mechanistic and empirical models for in vitro drug release, including zero-order, first-order, Higuchi, Korsmeyer-Peppas, Hixson-Crowell, and Weibull models. Pharmacokinetic functionality includes linear and nonlinear functions for one- and two-compartment models for intravenous bolus and oral administration, Michaelis-Menten kinetics, and non-compartmental analysis (NCA). Pharmacodynamic and dose-response modeling is supported through Emax-based models, including stimulatory (sigmoid Emax) and inhibitory (sigmoid Imax) Hill models, four- and five-parameter logistic models, as well as median toxic dose (TD50) and lethal dose (LD50) models. The package is intended to support parameter estimation, simulation, and model comparison in pharmaceutical research, drug development, and pharmacometrics education. For more details, see Gabrielsson & Weiner (2000) <ISBN:9186274929>, Holford & Sheiner (1981) <doi:10.2165/00003088-198106060-00002>, and Manlapaz (2025) <doi:10.32614/CRAN.package.adsoRptionCMF>.

The t-designs represent a generalized class of balanced incomplete block designs in which the number of blocks in which any t-tuple of treatments (t >= 2) occur together is a constant. When the focus of an experiment lies in grading and selecting treatment subgroups, t-designs would be preferred over the conventional ones, as they have the additional advantage of t-tuple balance. t-designs can be advantageously used in identifying the best crop-livestock combination for a particular location in Integrated Farming Systems that will help in generating maximum profit. But as the number of components increases, the number of possible t-component combinations will also increase. Most often, combinations derived from specific components are only practically feasible, for example, in a specific locality, farmers may not be interested in keeping a pig or goat and hence combinations involving these may not be of any use in that locality. In such situations partially balanced t-designs with few selected combinations appearing in a constant number of blocks (while others not at all appearing) may be useful (Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2021)<doi:10.1080/03610918.2021.2008436>). Further, every location may not have the resources to form equally sized homogeneous blocks. Partially balanced t-designs with unequal block sizes (Damaraju Raghavarao & Bei Zhou (1998)<doi:10.1080/03610929808832657>. Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2022)." Partially Balanced t-designs with unequal block sizes") prove to be more suitable for such situations.This package generates three series of partially balanced t-designs namely Series 1, Series 2 and Series 3. Series 1 and Series 2 are designs having equal block sizes and with treatment structures 4(t + 1) and a prime number, respectively. Series 3 consists of designs with unequal block sizes and with treatment structure n(n-1)/2. This package is based on the function named PBtD() for generating partially balanced t-designs along with their parameters, information matrices, average variance factors and canonical efficiency factors.

This package provides tools to interact with the Pangaea Database (<https://www.pangaea.de>), including functions for searching for data, fetching datasets by dataset ID', and working with the Pangaea OAI-PMH service.

Spectral response data for broadband ultraviolet and visible radiation sensors. Angular response data for broadband ultraviolet and visible radiation sensors and diffusers used as entrance optics. Data obtained from multiple sources were used: author-supplied data from scientific research papers, sensor-manufacturer supplied data, and published sensor specifications. Part of the r4photobiology suite Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for generating random data, fitting, predicting and check goodness of fit are prepared. The methods are developed from the theoretical framework of proportional hazard model and copula functions. Using this package, users can now simulate parametric time-dependent ROC and run experiment to understand the behavior of the curve under different scenario.