This package provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

This package contains an implementation of invariant causal prediction for sequential data. The main function in the package is seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines seqICP.s and seqICPnl.s corresponding to the respective main methods.

Detrending multivariate time-series to approximate stationarity when dealing with intensive longitudinal data, prior to Vector Autoregressive (VAR) or multilevel-VAR estimation. Classical VAR assumes weak stationarity (constant first two moments), and deterministic trends inflate spurious autocorrelation, biasing Granger-causality and impulse-response analyses. All functions operate on raw panel data and write detrended columns back to the data set, but differ in the level at which the trend is estimated. See, for instance, Wang & Maxwell (2015) <doi:10.1037/met0000030>; Burger et al. (2022) <doi:10.4324/9781003111238-13>; Epskamp et al. (2018) <doi:10.1177/2167702617744325>.

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).

Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ä evid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ä evid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. SDModels provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.1080/10618600.2025.2569602>).

This is a shape preserving spline <doi:10.1137/0720057> which is guaranteed to be monotonic and concave or convex if the data is monotonic and concave or convex. It does not use any optimisation and is therefore quick and smoothly converges to a fixed point in economic dynamics problems including value function iteration. It also automatically gives the first two derivatives of the spline and options for determining behaviour when evaluated outside the interpolation domain.

Sometimes it is useful to serve up alternative shiny UIs depending on information passed in the request object, such as the value of a cookie or a query parameter. This packages facilitates such switches.

Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45â 80.

Simulate event history data from a framework where treatment decisions and disease progression are represented as counting process. The user can specify number of events and parameters of intensities thereby creating a flexible simulation framework.

This package provides a collection of recycled and modified R functions to aid in file manipulation, data exploration, wrangling, optimization, and object manipulation. Other functions aid in convenient data visualization, loop progression, software packaging, and installation.

Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.

This package provides a collection of helper functions for forming bootstrapping confidence intervals and examining bootstrap estimates in structural equation modelling. Currently supports models fitted by the lavaan package by Rosseel (2012) <doi: 10.18637/jss.v048.i02>.

Scrap speech text and speaker informations of speeches of House of Representatives of Brazil, and transform in a cleaned tibble.

The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

Nonparametric method for testing the equality of the spectral densities of two time series of possibly different lengths. The time series are preprocessed with the discrete cosine transform and the variance stabilising transform to obtain an approximate Gaussian regression setting for the log-spectral density function. The test statistic is based on the squared L2 norm of the difference between the estimated log-spectral densities. The test returns the result, the statistic value, and the p-value. It also provides the estimated empirical quantile and null distribution under the hypothesis of equal spectral densities. An example using EEG data is included. For details see Nadin, Krivobokova, Enikeeva (2026), <doi:10.48550/arXiv.2602.10774>.

Create panel data consisting of independent states from 1816 to the present. The package includes the Gleditsch & Ward (G&W) and Correlates of War (COW) lists of independent states, as well as helper functions for working with state panel data and standardizing other data sources to create country-year/month/etc. data.

Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.

Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

This package provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) <doi:10.1093/biomet/86.1.59> and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published.

Estimation of functional linear mixed models for irregularly or sparsely sampled data based on functional principal component analysis.

Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)<doi:10.1080/01621459.2013.879531> proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion. Zang, Xu, and Burkett (2025)<doi:10.18637/jss.v115.i08>.