The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

This package provides functions for tabulating and summarising categorical variables. Most functions are designed to work with dataframes, and use the tidyverse idiom of taking the dataframe as the first argument so they work within pipelines. Equivalent functions that operate directly on vectors are also provided where it makes sense. This package aims to make exploratory data analysis involving categorical variables quicker, simpler and more robust.

Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.

Utilities for handling character vectors that store human-readable text (either plain or with markup, such as HTML or LaTeX). The package provides, in particular, functions that help with the preparation of plain-text reports, e.g. for expanding and aligning strings that form the lines of such reports. The package also provides generic functions for transforming R objects to HTML and to plain text.

Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.

Calculate optimal Zhong's two-/three-stage Phase II designs (see Zhong (2012) <doi:10.1016/j.cct.2012.07.006>). Generate Target Toxicity decision table for Phase I dose-finding (two-/three-stage). This package also allows users to run dose-finding simulations based on customized decision table.

Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.

Perform a Visual Predictive Check (VPC), while accounting for stratification, censoring, and prediction correction. Using piping from magrittr', the intuitive syntax gives users a flexible and powerful method to generate VPCs using both traditional binning and a new binless approach Jamsen et al. (2018) <doi:10.1002/psp4.12319> with Additive Quantile Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) prediction correction.

The United Nations Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The text2sdg package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information see Meier, Mata & Wulff (2025) <doi:10.32614/RJ-2024-005> and Wulff, Meier & Mata (2024) <doi:10.1007/s11625-024-01516-3>.

This package provides a constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license based on a different triangulation algorithm.

This interface was created to develop a standard procedure to analyse temporal trend in the framework of the OSPAR convention. The analysis process run through 4 successive steps : 1) manipulate your data, 2) select the parameters you want to analyse, 3) build your regulated time series, 4) perform diagnosis and analysis and 5) read the results. Statistical analysis call other package function such as Kendall tests or cusum() function.

This package implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood for uni- and multivariate mixture models using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Irons, Perrot-Dockès, Latouche & Raftery (2025) <doi:10.48550/arXiv.2504.21812>.

Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. TDApplied provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.

This package implements additional operators for computer vision models, including operators necessary for image segmentation and object detection deep learning models.

Dimensionality reduction (DR) is widely used in many domain for analyzing and visualizing high-dimensional data. tidydr provides uniform output and is compatible with multiple methods, including prcomp', mds', Rtsne'. etc.

This package performs model-based tensor clustering methods including Tensor Gaussian Mixture Model (TGMM), Tensor Envelope Mixture Model (TEMM) by Deng and Zhang (2021) <DOI: 10.1111/biom.13486>, Doubly-Enhanced EM (DEEM) algorithm by Mai, Zhang, Pan and Deng (2021) <DOI: 10.1080/01621459.2021.1904959>.

Instead of nesting function calls, annotate and transform functions using "#." comments.

Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. This package includes samplers powered by two techniques: analytic volume-preserving parameterizations, as employed by Arvo (1995) <doi:10.1145/218380.218500>, and rejection sampling, as employed by Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.

Implementation of Time-course Gene Set Analysis (TcGSA), a method for analyzing longitudinal gene-expression data at the gene set level. Method is detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.

Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944).

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including dplyr', stats', xts', forecast', slider', padr', recipes', and rsample'.

Defines a graphics device and functions for graphical output in terminal emulators that support graphical output. Currently terminals that support the Terminal Graphics Protocol (<https://sw.kovidgoyal.net/kitty/graphics-protocol/>) and terminal supporting Sixel (<https://en.wikipedia.org/wiki/Sixel>) are supported.

This package provides a collection of functions to plot acid/base titration curves (pH vs. volume of titrant), complexation titration curves (pMetal vs. volume of EDTA), redox titration curves (potential vs.volume of titrant), and precipitation titration curves (either pAnalyte or pTitrant vs. volume of titrant). Options include the titration of mixtures, the ability to overlay two or more titration curves, and the ability to show equivalence points.

This package implements methods to fit Virtual Twins models (Foster et al. (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.