Type:	Package
Title:	Interactive Tabular Matrix Problems via Pseudoinverse Estimation
Version:	0.1.0
Description:	Provides an interactive wrapper for the 'tmpinv()' function from the 'rtmpinv' package with options extending its functionality to pre- and post-estimation processing and streamlined incorporation of prior cell information. The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net).
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-US
Depends:	R (≥ 4.2)
Imports:	rtmpinv (≥ 0.2.0)
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
URL:	https://github.com/econcz/rtmpinvi
BugReports:	https://github.com/econcz/rtmpinvi/issues
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-02-20 15:27:13 UTC; ilyabolotov
Author:	Ilya Bolotov [aut, cre]
Maintainer:	Ilya Bolotov <ilya.bolotov@vse.cz>
Repository:	CRAN
Date/Publication:	2026-02-25 10:30:02 UTC

Solve an interactive tabular matrix estimation problem via Convex Least Squares Programming (CLSP).

Description

Solve an interactive tabular matrix estimation problem via Convex Least Squares Programming (CLSP).

Usage

tmpinvi(
  ival = NULL,
  ibounds = NULL,
  preestimation = NULL,
  postestimation = NULL,
  update = FALSE,
  ...
)

Arguments

Value

An object of class "tmpinvi" with components:

• result: a fitted object of class "tmpinv".

• data: the processed matrix (either the fitted solution x or the updated ival, depending on update).

See Also

tmpinv

Examples

  RNGkind("L'Ecuyer-CMRG")
  set.seed(123456789)

  iso2 <- c("CN", "DE", "JP", "NL", "US")
  T    <- 10L
  year <- (as.integer(format(Sys.Date(), "%Y")) - T) + seq_len(T)
  m    <- length(iso2)

  df <- expand.grid(year = year, iso2 = iso2, KEEP.OUT.ATTRS = FALSE)
  df <- df[order(df$year, df$iso2), ]

  ex_cols <- paste0("EX_", iso2)
  df[ex_cols] <- NA_real_
  df$EX <- NA_real_
  df$IM <- NA_real_

  X_true <- vector("list", length(year))
  names(X_true) <- as.character(year)

  for (t in seq_along(year)) {
    scale <- 1000 * (1.05^(t - 1L))
    X <- matrix(runif(m * m, 0, scale), m, m)
    diag(X) <- 0
    X_true[[t]] <- X

    rows <- ((t - 1L) * m + 1L):((t - 1L) * m + m)
    df$EX[rows] <- rowSums(X)
    df$IM[rows] <- colSums(X)

    miss <- matrix(runif(m * m) > 0.5, m, m)
    X[miss] <- NA_real_
    df[rows, ex_cols] <- X
  }

  cv <- qnorm(0.975)

  for (nm in ex_cols) {
    fit <- lm(df[[nm]] ~ year * iso2, data = df, na.action = na.exclude)
    pr  <- predict(fit, df, se.fit = TRUE)
    ub  <- pr$fit + cv * pr$se.fit
    ub[ub < 0] <- NA_real_
    df[[paste0("_", nm, "_lb")]] <- 0
    df[[paste0("_", nm, "_ub")]] <- ub
  }

  make_bounds <- function(lb, ub)
    Map(function(a, b) c(a, b), lb, ub)

  df_out <- df

  for (step in 1:2) {
    for (y in year) {
      idx  <- df_out$year == y
      d    <- df_out[idx, ]
      ival <- as.matrix(d[ex_cols])

      lb <- as.vector(t(as.matrix(d[paste0("_EX_", iso2, "_lb")])))
      ub <- as.vector(t(as.matrix(d[paste0("_EX_", iso2, "_ub")])))

      fit <- tmpinvi(
        ival = ival,
        ibounds = make_bounds(lb, ub),
        b_row = d$EX,
        b_col = d$IM,
        alpha = 1.0,
        update = TRUE
      )

      df_out[idx, ex_cols] <- fit$data
    }
  }

  drop_cols <- grep("^_EX_.*_(lb|ub)$", names(df_out), value = TRUE)
  df_out[drop_cols] <- NULL
  df_out