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 ORCID iD [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

ival

NULL, numeric matrix, or data.frame. Prior information on known cell values. If supplied and not entirely missing, ival is flattened and used to construct b_val and the corresponding identity-subset model matrix M internally. Missing entries (NA) are ignored. If all entries of ival are NA, no prior information is used and b_val and M are set to NULL. When ival is provided, it overrides any b_val or M arguments supplied through ....

ibounds

NULL, numeric(2), or list of numeric(2). Dynamic cell-value bounds passed to tmpinv(bounds = ...). The object supplied to ibounds may be created or modified programmatically (for example within preestimation()). If a single pair c(low, high) is provided, it is applied uniformly to all cells. Alternatively, a list of pairs may be supplied to specify cell-specific bounds with others set to NA. When ibounds is non-NULL, it overrides any bounds argument supplied through ....

preestimation

NULL or function. A function executed prior to model estimation. If supplied, it is called as preestimation(ival) and may perform arbitrary preparatory steps, such as constructing dynamic bounds or modifying objects in the calling environment. The return value is ignored.

postestimation

NULL or function. A function executed after model estimation. For a full model, it is called as postestimation(model). For reduced (block-wise) models, it is called as postestimation(model[[i]], i) for each block index i. The return value is ignored.

update

logical scalar, default = FALSE. If TRUE and ival is supplied, missing entries (NA) in ival are replaced by the corresponding fitted values from tmpinvi$result$x. The updated matrix is returned in the tmpinvi$data component. If FALSE, the data component contains the fitted solution matrix tmpinvi$result$x.

...

Additional arguments passed to rtmpinv::tmpinv().

Value

An object of class "tmpinvi" with components:

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