It implements the Nelson-Siegel and the Nelson-Siegel-Svensson term structures.

Connection to the Fitbit Web API <https://dev.fitbit.com/build/reference/web-api/> by including ggplot2 Visualizations, Leaflet and 3-dimensional Rayshader Maps. The 3-dimensional Rayshader Map requires the installation of the CopernicusDEM R package which includes the 30- and 90-meter elevation data.

This package creates a full rank matrix out of a given matrix. The intended use is for one-hot encoded design matrices that should be used in linear models to ensure that significant associations can be correctly interpreted. However, fullRankMatrix can be applied to any matrix to make it full rank. It removes columns with only 0's, merges duplicated columns and discovers linearly dependent columns and replaces them with linearly independent columns that span the space of the original columns. Columns are renamed to reflect those modifications. This results in a full rank matrix that can be used as a design matrix in linear models. The algorithm and some functions are inspired by Kuhn, M. (2008) <doi:10.18637/jss.v028.i05>.

Estimation of functional spaces based on traits of organisms. The package includes functions to impute missing trait values (with or without considering phylogenetic information), and to create, represent and analyse two dimensional functional spaces based on principal components analysis, other ordination methods, or raw traits. It also allows for mapping a third variable onto the functional space. See Carmona et al. (2021) <doi:10.1038/s41586-021-03871-y>, Puglielli et al. (2021) <doi:10.1111/nph.16952>, Carmona et al. (2021) <doi:10.1126/sciadv.abf2675>, Carmona et al. (2019) <doi:10.1002/ecy.2876> for more information.

Easily use Font Awesome icons as shiny favicons (the icons that appear on browser tabs). Font Awesome (<https://fontawesome.com/>) is a popular set of icons that can be used in web pages. favawesome provides a simple way to use these icons as favicons in shiny applications and other HTML pages.

Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2020) <doi:10.1214/20-EJS1771>. The main algorithms of the paper are available as continuous, discrete and weighted versions. They take as input the results of a test procedure from package DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with wrappers allowing to apply discrete procedures directly to data.

The main functions in this package are with_cache() and cached_read(). The former is a simple way to cache an R object into a file on disk, using cachem'. The latter is a wrapper around any standard read function, but caches both the output and the file list info. If the input file list info hasn't changed, the cache is used; otherwise, the original files are re-read. This can save time if the original operation requires reading from many files, and/or involves lots of processing.

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.

Brings a set of tools to help and automatically realise the description of principal component analyses (from FactoMineR functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).

R API client package for Fingrid Open Data <https://data.fingrid.fi/> on the electricity market and the power system. get_data() function holds the main application logic to retrieve time-series data. API calls require free user account registration. Data is made available by Fingrid Oyj and distributed under Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.

Emulates a Forth programming environment with added features to interface between R and Forth'. Implements most of the functionality described in the original "Starting Forth" textbook <https://www.forth.com/starting-forth/>.

R implementations of standard financial engineering codes; vanilla option pricing models such as Black-Scholes, Bachelier, CEV, and SABR.

Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.

This package provides a collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.

Extends the fitdist() (from fitdistrplus') adding the Anderson-Darling ad.test() (from ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from stats package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from mclust').

Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official API, but instead uses the API that the web dashboard uses to generate the graphs displayed on the dashboard after login at <http://www.fitbit.com>.

We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, <doi:10.18637/jss.v096.i07>.

Estimate parameters of univariate probability distributions with maximum likelihood and Bayesian methods.

Find functions in an unstructured directory and explore their dependencies. Sourcing of R source files is performed without side-effects: from R scripts that have executable code and function definitions only functions are sourced.

An implementation of the two-sample multivariate Kolmogorov-Smirnov test described by Fasano and Franceschini (1987) <doi:10.1093/mnras/225.1.155>. This test evaluates the null hypothesis that two i.i.d. random samples were drawn from the same underlying probability distribution. The data can be of any dimension, and can be of any type (continuous, discrete, or mixed).

Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) <doi:10.1177/1471082X211034048> for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) <doi:10.18637/jss.v109.i09>.

Toolbox to process raw data from closed loop flux chamber (or tent) setups into ecosystem gas fluxes usable for analysis. It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Organized with one function per step, maximizing user flexibility and backwards compatibility. Different models to estimate the fluxes from the raw data are available: exponential as described in Zhao et al (2018) <doi:10.1016/j.agrformet.2018.08.022>, exponential as described in Hutchinson and Mosier (1981) <doi:10.2136/sssaj1981.03615995004500020017x>, quadratic, and linear. Other functions include quality assessment, plotting for visual check, calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...), gross primary production and transpiration rate calculation, and light response curves.

This package provides tools and features for "Exploratory Landscape Analysis (ELA)" of single-objective continuous optimization problems. Those features are able to quantify rather complex properties, such as the global structure, separability, etc., of the optimization problems.

Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply ForeCA to multivariate time series data. ForeCA is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as PCA or ICA', ForeCA takes time dependency explicitly into account and searches for the most forecastable signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.